Chart,Question,Id
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as <=50K.",0
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as <=50K.",1
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as <=50K.",2
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as <=50K.",3
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as >50K.",4
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as >50K.",5
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as >50K.",6
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as >50K.",7
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as <=50K for any k ≤ 21974.",8
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as <=50K for any k ≤ 21974.",9
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as <=50K for any k ≤ 21974.",10
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as <=50K for any k ≤ 21974.",11
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as >50K for any k ≤ 21974.",12
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as >50K for any k ≤ 21974.",13
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as >50K for any k ≤ 21974.",14
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as >50K for any k ≤ 21974.",15
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as <=50K for any k ≤ 541.",16
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as <=50K for any k ≤ 541.",17
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as <=50K for any k ≤ 541.",18
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as <=50K for any k ≤ 541.",19
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as >50K for any k ≤ 541.",20
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as >50K for any k ≤ 541.",21
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as >50K for any k ≤ 541.",22
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as >50K for any k ≤ 541.",23
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as <=50K for any k ≤ 9274.",24
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as <=50K for any k ≤ 9274.",25
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as <=50K for any k ≤ 9274.",26
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as <=50K for any k ≤ 9274.",27
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as >50K for any k ≤ 9274.",28
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as >50K for any k ≤ 9274.",29
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as >50K for any k ≤ 9274.",30
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as >50K for any k ≤ 9274.",31
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as <=50K for any k ≤ 434.",32
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as <=50K for any k ≤ 434.",33
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as <=50K for any k ≤ 434.",34
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as <=50K for any k ≤ 434.",35
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as >50K for any k ≤ 434.",36
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as >50K for any k ≤ 434.",37
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as >50K for any k ≤ 434.",38
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as >50K for any k ≤ 434.",39
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (A,B) as <=50K.",40
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (not A, B) as <=50K.",41
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (A, not B) as <=50K.",42
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (not A, not B) as <=50K.",43
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (A,B) as >50K.",44
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (not A, B) as >50K.",45
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (A, not B) as >50K.",46
adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (not A, not B) as >50K.",47
adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1498 episodes.,48
adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 717 episodes.,49
adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 566 episodes.,50
adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 657 episodes.,51
adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 936 episodes.,52
adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 133 estimators.,53
adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 54 estimators.,54
adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 76 estimators.,55
adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 119 estimators.,56
adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 146 estimators.,57
adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 102 estimators.,58
adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 95 estimators.,59
adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 122 estimators.,60
adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 112 estimators.,61
adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 91 estimators.,62
adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,63
adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,64
adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,65
adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,66
adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,67
adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,68
adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,69
adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,70
adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,71
adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,72
adult_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,73
adult_decision_tree.png,The recall for the presented tree is higher than its accuracy.,74
adult_decision_tree.png,The precision for the presented tree is higher than its accuracy.,75
adult_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,76
adult_decision_tree.png,The recall for the presented tree is lower than its accuracy.,77
adult_decision_tree.png,The precision for the presented tree is lower than its accuracy.,78
adult_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,79
adult_decision_tree.png,The accuracy for the presented tree is higher than its recall.,80
adult_decision_tree.png,The precision for the presented tree is higher than its recall.,81
adult_decision_tree.png,The specificity for the presented tree is higher than its recall.,82
adult_decision_tree.png,The accuracy for the presented tree is lower than its recall.,83
adult_decision_tree.png,The precision for the presented tree is lower than its recall.,84
adult_decision_tree.png,The specificity for the presented tree is lower than its recall.,85
adult_decision_tree.png,The accuracy for the presented tree is higher than its precision.,86
adult_decision_tree.png,The recall for the presented tree is higher than its precision.,87
adult_decision_tree.png,The specificity for the presented tree is higher than its precision.,88
adult_decision_tree.png,The accuracy for the presented tree is lower than its precision.,89
adult_decision_tree.png,The recall for the presented tree is lower than its precision.,90
adult_decision_tree.png,The specificity for the presented tree is lower than its precision.,91
adult_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,92
adult_decision_tree.png,The recall for the presented tree is higher than its specificity.,93
adult_decision_tree.png,The precision for the presented tree is higher than its specificity.,94
adult_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,95
adult_decision_tree.png,The recall for the presented tree is lower than its specificity.,96
adult_decision_tree.png,The precision for the presented tree is lower than its specificity.,97
adult_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,98
adult_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,99
adult_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,100
adult_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,101
adult_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,102
adult_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,103
adult_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,104
adult_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,105
adult_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,106
adult_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,107
adult_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,108
adult_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,109
adult_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,110
adult_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,111
adult_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,112
adult_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,113
adult_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,114
adult_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,115
adult_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,116
adult_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,117
adult_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,118
adult_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,119
adult_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,120
adult_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,121
adult_decision_tree.png,The number of True Positives reported in the same tree is 12.,122
adult_decision_tree.png,The number of False Positives reported in the same tree is 35.,123
adult_decision_tree.png,The number of True Negatives reported in the same tree is 37.,124
adult_decision_tree.png,The number of False Negatives reported in the same tree is 15.,125
adult_decision_tree.png,The number of True Positives reported in the same tree is 17.,126
adult_decision_tree.png,The number of False Positives reported in the same tree is 50.,127
adult_decision_tree.png,The number of True Negatives reported in the same tree is 11.,128
adult_decision_tree.png,The number of False Negatives reported in the same tree is 41.,129
adult_decision_tree.png,The number of True Positives reported in the same tree is 25.,130
adult_decision_tree.png,The number of False Positives reported in the same tree is 18.,131
adult_decision_tree.png,The number of True Negatives reported in the same tree is 28.,132
adult_decision_tree.png,The number of False Negatives reported in the same tree is 33.,133
adult_decision_tree.png,The number of True Positives reported in the same tree is 34.,134
adult_decision_tree.png,The number of False Positives reported in the same tree is 45.,135
adult_decision_tree.png,The number of True Negatives reported in the same tree is 22.,136
adult_decision_tree.png,The number of False Negatives reported in the same tree is 30.,137
adult_decision_tree.png,The number of True Positives reported in the same tree is 23.,138
adult_decision_tree.png,The number of False Positives reported in the same tree is 21.,139
adult_decision_tree.png,The number of True Negatives reported in the same tree is 39.,140
adult_decision_tree.png,The number of False Negatives reported in the same tree is 40.,141
adult_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,142
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,143
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,144
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,145
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,146
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,147
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,148
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,149
adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,150
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,151
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,152
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,153
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,154
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,155
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,156
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,157
adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,158
adult_decision_tree.png,The accuracy for the presented tree is higher than 63%.,159
adult_decision_tree.png,The recall for the presented tree is higher than 90%.,160
adult_decision_tree.png,The precision for the presented tree is higher than 73%.,161
adult_decision_tree.png,The specificity for the presented tree is higher than 87%.,162
adult_decision_tree.png,The accuracy for the presented tree is lower than 64%.,163
adult_decision_tree.png,The recall for the presented tree is lower than 79%.,164
adult_decision_tree.png,The precision for the presented tree is lower than 70%.,165
adult_decision_tree.png,The specificity for the presented tree is lower than 62%.,166
adult_decision_tree.png,The accuracy for the presented tree is higher than 71%.,167
adult_decision_tree.png,The recall for the presented tree is higher than 85%.,168
adult_decision_tree.png,The precision for the presented tree is higher than 83%.,169
adult_decision_tree.png,The specificity for the presented tree is higher than 89%.,170
adult_decision_tree.png,The accuracy for the presented tree is lower than 84%.,171
adult_decision_tree.png,The recall for the presented tree is lower than 76%.,172
adult_decision_tree.png,The precision for the presented tree is lower than 61%.,173
adult_decision_tree.png,The specificity for the presented tree is lower than 75%.,174
adult_decision_tree.png,The accuracy for the presented tree is higher than 65%.,175
adult_decision_tree.png,The recall for the presented tree is higher than 80%.,176
adult_decision_tree.png,The precision for the presented tree is higher than 60%.,177
adult_decision_tree.png,The specificity for the presented tree is higher than 72%.,178
adult_decision_tree.png,The accuracy for the presented tree is lower than 67%.,179
adult_decision_tree.png,The recall for the presented tree is lower than 68%.,180
adult_decision_tree.png,The precision for the presented tree is lower than 69%.,181
adult_decision_tree.png,The specificity for the presented tree is lower than 78%.,182
adult_decision_tree.png,The accuracy for the presented tree is higher than 88%.,183
adult_decision_tree.png,The recall for the presented tree is higher than 66%.,184
adult_decision_tree.png,The precision for the presented tree is higher than 77%.,185
adult_decision_tree.png,The specificity for the presented tree is higher than 74%.,186
adult_decision_tree.png,The accuracy for the presented tree is lower than 81%.,187
adult_decision_tree.png,The recall for the presented tree is lower than 82%.,188
adult_decision_tree.png,The precision for the presented tree is lower than 86%.,189
adult_decision_tree.png,The specificity for the presented tree is lower than 61%.,190
adult_decision_tree.png,The accuracy for the presented tree is higher than 87%.,191
adult_decision_tree.png,The recall for the presented tree is higher than 62%.,192
adult_decision_tree.png,The precision for the presented tree is higher than 70%.,193
adult_decision_tree.png,The specificity for the presented tree is higher than 71%.,194
adult_decision_tree.png,The accuracy for the presented tree is lower than 66%.,195
adult_decision_tree.png,The recall for the presented tree is lower than 68%.,196
adult_decision_tree.png,The precision for the presented tree is lower than 73%.,197
adult_decision_tree.png,The specificity for the presented tree is lower than 84%.,198
adult_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",199
adult_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",200
adult_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",201
adult_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",202
adult_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",203
adult_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",204
adult_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,205
adult_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,206
adult_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,207
adult_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,208
adult_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,209
adult_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,210
adult_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,211
adult_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,212
adult_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,213
adult_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,214
adult_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,215
adult_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,216
adult_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,217
adult_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,218
adult_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,219
adult_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,220
adult_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,221
adult_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,222
adult_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,223
adult_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,224
adult_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,225
adult_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,226
adult_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,227
adult_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,228
adult_overfitting_knn.png,KNN is in overfitting for k less than 2.,229
adult_overfitting_knn.png,KNN is in overfitting for k larger than 2.,230
adult_overfitting_knn.png,KNN is in overfitting for k less than 3.,231
adult_overfitting_knn.png,KNN is in overfitting for k larger than 3.,232
adult_overfitting_knn.png,KNN is in overfitting for k less than 4.,233
adult_overfitting_knn.png,KNN is in overfitting for k larger than 4.,234
adult_overfitting_knn.png,KNN is in overfitting for k less than 5.,235
adult_overfitting_knn.png,KNN is in overfitting for k larger than 5.,236
adult_overfitting_knn.png,KNN is in overfitting for k less than 6.,237
adult_overfitting_knn.png,KNN is in overfitting for k larger than 6.,238
adult_overfitting_knn.png,KNN is in overfitting for k less than 7.,239
adult_overfitting_knn.png,KNN is in overfitting for k larger than 7.,240
adult_overfitting_knn.png,KNN is in overfitting for k less than 8.,241
adult_overfitting_knn.png,KNN is in overfitting for k larger than 8.,242
adult_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",243
adult_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",244
adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",245
adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",246
adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",247
adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",248
adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",249
adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",250
adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",251
adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",252
adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",253
adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",254
adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",255
adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",256
adult_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,257
adult_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,258
adult_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,259
adult_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,260
adult_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,261
adult_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,262
adult_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,263
adult_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,264
adult_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,265
adult_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,266
adult_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,267
adult_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,268
adult_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,269
adult_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,270
adult_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,271
adult_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,272
adult_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,273
adult_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,274
adult_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,275
adult_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,276
adult_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,277
adult_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,278
adult_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,279
adult_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,280
adult_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,281
adult_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,282
adult_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,283
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fnlwgt previously than variable age.,284
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable educational-num previously than variable age.,285
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-gain previously than variable age.,286
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-loss previously than variable age.,287
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable hours-per-week previously than variable age.,288
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable fnlwgt.,289
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable educational-num previously than variable fnlwgt.,290
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-gain previously than variable fnlwgt.,291
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-loss previously than variable fnlwgt.,292
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable hours-per-week previously than variable fnlwgt.,293
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable educational-num.,294
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fnlwgt previously than variable educational-num.,295
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-gain previously than variable educational-num.,296
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-loss previously than variable educational-num.,297
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable hours-per-week previously than variable educational-num.,298
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable capital-gain.,299
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fnlwgt previously than variable capital-gain.,300
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable educational-num previously than variable capital-gain.,301
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-loss previously than variable capital-gain.,302
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable hours-per-week previously than variable capital-gain.,303
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable capital-loss.,304
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fnlwgt previously than variable capital-loss.,305
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable educational-num previously than variable capital-loss.,306
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-gain previously than variable capital-loss.,307
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable hours-per-week previously than variable capital-loss.,308
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable hours-per-week.,309
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fnlwgt previously than variable hours-per-week.,310
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable educational-num previously than variable hours-per-week.,311
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-gain previously than variable hours-per-week.,312
adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-loss previously than variable hours-per-week.,313
adult_pca.png,The first 2 principal components are enough for explaining half the data variance.,314
adult_pca.png,The first 3 principal components are enough for explaining half the data variance.,315
adult_pca.png,The first 4 principal components are enough for explaining half the data variance.,316
adult_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,317
adult_correlation_heatmap.png,Removing variable age might improve the training of decision trees .,318
adult_correlation_heatmap.png,Removing variable fnlwgt might improve the training of decision trees .,319
adult_correlation_heatmap.png,Removing variable educational-num might improve the training of decision trees .,320
adult_correlation_heatmap.png,Removing variable capital-gain might improve the training of decision trees .,321
adult_correlation_heatmap.png,Removing variable capital-loss might improve the training of decision trees .,322
adult_correlation_heatmap.png,Removing variable hours-per-week might improve the training of decision trees .,323
adult_histograms.png,"Not knowing the semantics of age variable, dummification could have been a more adequate codification.",324
adult_histograms.png,"Not knowing the semantics of workclass variable, dummification could have been a more adequate codification.",325
adult_histograms.png,"Not knowing the semantics of fnlwgt variable, dummification could have been a more adequate codification.",326
adult_histograms.png,"Not knowing the semantics of education variable, dummification could have been a more adequate codification.",327
adult_histograms.png,"Not knowing the semantics of educational-num variable, dummification could have been a more adequate codification.",328
adult_histograms.png,"Not knowing the semantics of marital-status variable, dummification could have been a more adequate codification.",329
adult_histograms.png,"Not knowing the semantics of occupation variable, dummification could have been a more adequate codification.",330
adult_histograms.png,"Not knowing the semantics of relationship variable, dummification could have been a more adequate codification.",331
adult_histograms.png,"Not knowing the semantics of race variable, dummification could have been a more adequate codification.",332
adult_histograms.png,"Not knowing the semantics of gender variable, dummification could have been a more adequate codification.",333
adult_histograms.png,"Not knowing the semantics of capital-gain variable, dummification could have been a more adequate codification.",334
adult_histograms.png,"Not knowing the semantics of capital-loss variable, dummification could have been a more adequate codification.",335
adult_histograms.png,"Not knowing the semantics of hours-per-week variable, dummification could have been a more adequate codification.",336
adult_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,337
adult_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",338
adult_histograms.png,It is better to drop the variable age than removing all records with missing values.,339
adult_histograms.png,It is better to drop the variable workclass than removing all records with missing values.,340
adult_histograms.png,It is better to drop the variable fnlwgt than removing all records with missing values.,341
adult_histograms.png,It is better to drop the variable education than removing all records with missing values.,342
adult_histograms.png,It is better to drop the variable educational-num than removing all records with missing values.,343
adult_histograms.png,It is better to drop the variable marital-status than removing all records with missing values.,344
adult_histograms.png,It is better to drop the variable occupation than removing all records with missing values.,345
adult_histograms.png,It is better to drop the variable relationship than removing all records with missing values.,346
adult_histograms.png,It is better to drop the variable race than removing all records with missing values.,347
adult_histograms.png,It is better to drop the variable gender than removing all records with missing values.,348
adult_histograms.png,It is better to drop the variable capital-gain than removing all records with missing values.,349
adult_histograms.png,It is better to drop the variable capital-loss than removing all records with missing values.,350
adult_histograms.png,It is better to drop the variable hours-per-week than removing all records with missing values.,351
adult_histograms.png,"Given the usual semantics of age variable, dummification would have been a better codification.",352
adult_histograms.png,"Given the usual semantics of workclass variable, dummification would have been a better codification.",353
adult_histograms.png,"Given the usual semantics of fnlwgt variable, dummification would have been a better codification.",354
adult_histograms.png,"Given the usual semantics of education variable, dummification would have been a better codification.",355
adult_histograms.png,"Given the usual semantics of educational-num variable, dummification would have been a better codification.",356
adult_histograms.png,"Given the usual semantics of marital-status variable, dummification would have been a better codification.",357
adult_histograms.png,"Given the usual semantics of occupation variable, dummification would have been a better codification.",358
adult_histograms.png,"Given the usual semantics of relationship variable, dummification would have been a better codification.",359
adult_histograms.png,"Given the usual semantics of race variable, dummification would have been a better codification.",360
adult_histograms.png,"Given the usual semantics of gender variable, dummification would have been a better codification.",361
adult_histograms.png,"Given the usual semantics of capital-gain variable, dummification would have been a better codification.",362
adult_histograms.png,"Given the usual semantics of capital-loss variable, dummification would have been a better codification.",363
adult_histograms.png,"Given the usual semantics of hours-per-week variable, dummification would have been a better codification.",364
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of age seems to be promising.",365
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of age seems to be promising.",366
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of age seems to be promising.",367
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of age seems to be promising.",368
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of age seems to be promising.",369
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of age seems to be promising.",370
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of age seems to be promising.",371
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of age seems to be promising.",372
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of age seems to be promising.",373
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of age seems to be promising.",374
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of age seems to be promising.",375
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of age seems to be promising.",376
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of workclass seems to be promising.",377
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of workclass seems to be promising.",378
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of workclass seems to be promising.",379
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of workclass seems to be promising.",380
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of workclass seems to be promising.",381
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of workclass seems to be promising.",382
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of workclass seems to be promising.",383
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of workclass seems to be promising.",384
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of workclass seems to be promising.",385
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of workclass seems to be promising.",386
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of workclass seems to be promising.",387
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of workclass seems to be promising.",388
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of fnlwgt seems to be promising.",389
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of fnlwgt seems to be promising.",390
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of fnlwgt seems to be promising.",391
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of fnlwgt seems to be promising.",392
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of fnlwgt seems to be promising.",393
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of fnlwgt seems to be promising.",394
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of fnlwgt seems to be promising.",395
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of fnlwgt seems to be promising.",396
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of fnlwgt seems to be promising.",397
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of fnlwgt seems to be promising.",398
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of fnlwgt seems to be promising.",399
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of fnlwgt seems to be promising.",400
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of education seems to be promising.",401
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of education seems to be promising.",402
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of education seems to be promising.",403
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of education seems to be promising.",404
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of education seems to be promising.",405
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of education seems to be promising.",406
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of education seems to be promising.",407
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of education seems to be promising.",408
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of education seems to be promising.",409
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of education seems to be promising.",410
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of education seems to be promising.",411
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of educational-num seems to be promising.",412
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of educational-num seems to be promising.",413
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of educational-num seems to be promising.",414
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of educational-num seems to be promising.",415
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of educational-num seems to be promising.",416
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of educational-num seems to be promising.",417
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of educational-num seems to be promising.",418
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of educational-num seems to be promising.",419
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of educational-num seems to be promising.",420
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of educational-num seems to be promising.",421
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of educational-num seems to be promising.",422
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of educational-num seems to be promising.",423
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of marital-status seems to be promising.",424
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of marital-status seems to be promising.",425
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of marital-status seems to be promising.",426
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of marital-status seems to be promising.",427
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of marital-status seems to be promising.",428
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of marital-status seems to be promising.",429
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of marital-status seems to be promising.",430
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of marital-status seems to be promising.",431
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of marital-status seems to be promising.",432
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of marital-status seems to be promising.",433
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of marital-status seems to be promising.",434
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of marital-status seems to be promising.",435
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of occupation seems to be promising.",436
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of occupation seems to be promising.",437
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of occupation seems to be promising.",438
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of occupation seems to be promising.",439
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of occupation seems to be promising.",440
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of occupation seems to be promising.",441
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of occupation seems to be promising.",442
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of occupation seems to be promising.",443
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of occupation seems to be promising.",444
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of occupation seems to be promising.",445
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of occupation seems to be promising.",446
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of occupation seems to be promising.",447
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of relationship seems to be promising.",448
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of relationship seems to be promising.",449
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of relationship seems to be promising.",450
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of relationship seems to be promising.",451
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of relationship seems to be promising.",452
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of relationship seems to be promising.",453
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of relationship seems to be promising.",454
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of relationship seems to be promising.",455
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of relationship seems to be promising.",456
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of relationship seems to be promising.",457
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of relationship seems to be promising.",458
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of relationship seems to be promising.",459
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of race seems to be promising.",460
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of race seems to be promising.",461
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of race seems to be promising.",462
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of race seems to be promising.",463
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of race seems to be promising.",464
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of race seems to be promising.",465
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of race seems to be promising.",466
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of race seems to be promising.",467
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of race seems to be promising.",468
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of race seems to be promising.",469
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of race seems to be promising.",470
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of race seems to be promising.",471
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of gender seems to be promising.",472
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of gender seems to be promising.",473
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of gender seems to be promising.",474
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of gender seems to be promising.",475
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of gender seems to be promising.",476
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of gender seems to be promising.",477
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of gender seems to be promising.",478
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of gender seems to be promising.",479
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of gender seems to be promising.",480
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of gender seems to be promising.",481
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of gender seems to be promising.",482
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of gender seems to be promising.",483
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of capital-gain seems to be promising.",484
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of capital-gain seems to be promising.",485
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of capital-gain seems to be promising.",486
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of capital-gain seems to be promising.",487
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of capital-gain seems to be promising.",488
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of capital-gain seems to be promising.",489
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of capital-gain seems to be promising.",490
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of capital-gain seems to be promising.",491
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of capital-gain seems to be promising.",492
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of capital-gain seems to be promising.",493
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of capital-gain seems to be promising.",494
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of capital-gain seems to be promising.",495
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of capital-loss seems to be promising.",496
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of capital-loss seems to be promising.",497
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of capital-loss seems to be promising.",498
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of capital-loss seems to be promising.",499
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of capital-loss seems to be promising.",500
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of capital-loss seems to be promising.",501
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of capital-loss seems to be promising.",502
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of capital-loss seems to be promising.",503
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of capital-loss seems to be promising.",504
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of capital-loss seems to be promising.",505
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of capital-loss seems to be promising.",506
adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of capital-loss seems to be promising.",507
adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of hours-per-week seems to be promising.",508
adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of hours-per-week seems to be promising.",509
adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of hours-per-week seems to be promising.",510
adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of hours-per-week seems to be promising.",511
adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of hours-per-week seems to be promising.",512
adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of hours-per-week seems to be promising.",513
adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of hours-per-week seems to be promising.",514
adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of hours-per-week seems to be promising.",515
adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of hours-per-week seems to be promising.",516
adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of hours-per-week seems to be promising.",517
adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of hours-per-week seems to be promising.",518
adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of hours-per-week seems to be promising.",519
adult_histograms.png,Feature generation based on both variables workclass and age seems to be promising.,520
adult_histograms.png,Feature generation based on both variables fnlwgt and age seems to be promising.,521
adult_histograms.png,Feature generation based on both variables education and age seems to be promising.,522
adult_histograms.png,Feature generation based on both variables educational-num and age seems to be promising.,523
adult_histograms.png,Feature generation based on both variables marital-status and age seems to be promising.,524
adult_histograms.png,Feature generation based on both variables occupation and age seems to be promising.,525
adult_histograms.png,Feature generation based on both variables relationship and age seems to be promising.,526
adult_histograms.png,Feature generation based on both variables race and age seems to be promising.,527
adult_histograms.png,Feature generation based on both variables gender and age seems to be promising.,528
adult_histograms.png,Feature generation based on both variables capital-gain and age seems to be promising.,529
adult_histograms.png,Feature generation based on both variables capital-loss and age seems to be promising.,530
adult_histograms.png,Feature generation based on both variables hours-per-week and age seems to be promising.,531
adult_histograms.png,Feature generation based on both variables age and workclass seems to be promising.,532
adult_histograms.png,Feature generation based on both variables fnlwgt and workclass seems to be promising.,533
adult_histograms.png,Feature generation based on both variables education and workclass seems to be promising.,534
adult_histograms.png,Feature generation based on both variables educational-num and workclass seems to be promising.,535
adult_histograms.png,Feature generation based on both variables marital-status and workclass seems to be promising.,536
adult_histograms.png,Feature generation based on both variables occupation and workclass seems to be promising.,537
adult_histograms.png,Feature generation based on both variables relationship and workclass seems to be promising.,538
adult_histograms.png,Feature generation based on both variables race and workclass seems to be promising.,539
adult_histograms.png,Feature generation based on both variables gender and workclass seems to be promising.,540
adult_histograms.png,Feature generation based on both variables capital-gain and workclass seems to be promising.,541
adult_histograms.png,Feature generation based on both variables capital-loss and workclass seems to be promising.,542
adult_histograms.png,Feature generation based on both variables hours-per-week and workclass seems to be promising.,543
adult_histograms.png,Feature generation based on both variables age and fnlwgt seems to be promising.,544
adult_histograms.png,Feature generation based on both variables workclass and fnlwgt seems to be promising.,545
adult_histograms.png,Feature generation based on both variables education and fnlwgt seems to be promising.,546
adult_histograms.png,Feature generation based on both variables educational-num and fnlwgt seems to be promising.,547
adult_histograms.png,Feature generation based on both variables marital-status and fnlwgt seems to be promising.,548
adult_histograms.png,Feature generation based on both variables occupation and fnlwgt seems to be promising.,549
adult_histograms.png,Feature generation based on both variables relationship and fnlwgt seems to be promising.,550
adult_histograms.png,Feature generation based on both variables race and fnlwgt seems to be promising.,551
adult_histograms.png,Feature generation based on both variables gender and fnlwgt seems to be promising.,552
adult_histograms.png,Feature generation based on both variables capital-gain and fnlwgt seems to be promising.,553
adult_histograms.png,Feature generation based on both variables capital-loss and fnlwgt seems to be promising.,554
adult_histograms.png,Feature generation based on both variables hours-per-week and fnlwgt seems to be promising.,555
adult_histograms.png,Feature generation based on both variables age and education seems to be promising.,556
adult_histograms.png,Feature generation based on both variables workclass and education seems to be promising.,557
adult_histograms.png,Feature generation based on both variables fnlwgt and education seems to be promising.,558
adult_histograms.png,Feature generation based on both variables marital-status and education seems to be promising.,559
adult_histograms.png,Feature generation based on both variables occupation and education seems to be promising.,560
adult_histograms.png,Feature generation based on both variables relationship and education seems to be promising.,561
adult_histograms.png,Feature generation based on both variables race and education seems to be promising.,562
adult_histograms.png,Feature generation based on both variables gender and education seems to be promising.,563
adult_histograms.png,Feature generation based on both variables capital-gain and education seems to be promising.,564
adult_histograms.png,Feature generation based on both variables capital-loss and education seems to be promising.,565
adult_histograms.png,Feature generation based on both variables hours-per-week and education seems to be promising.,566
adult_histograms.png,Feature generation based on both variables age and educational-num seems to be promising.,567
adult_histograms.png,Feature generation based on both variables workclass and educational-num seems to be promising.,568
adult_histograms.png,Feature generation based on both variables fnlwgt and educational-num seems to be promising.,569
adult_histograms.png,Feature generation based on both variables education and educational-num seems to be promising.,570
adult_histograms.png,Feature generation based on both variables marital-status and educational-num seems to be promising.,571
adult_histograms.png,Feature generation based on both variables occupation and educational-num seems to be promising.,572
adult_histograms.png,Feature generation based on both variables relationship and educational-num seems to be promising.,573
adult_histograms.png,Feature generation based on both variables race and educational-num seems to be promising.,574
adult_histograms.png,Feature generation based on both variables gender and educational-num seems to be promising.,575
adult_histograms.png,Feature generation based on both variables capital-gain and educational-num seems to be promising.,576
adult_histograms.png,Feature generation based on both variables capital-loss and educational-num seems to be promising.,577
adult_histograms.png,Feature generation based on both variables hours-per-week and educational-num seems to be promising.,578
adult_histograms.png,Feature generation based on both variables age and marital-status seems to be promising.,579
adult_histograms.png,Feature generation based on both variables workclass and marital-status seems to be promising.,580
adult_histograms.png,Feature generation based on both variables fnlwgt and marital-status seems to be promising.,581
adult_histograms.png,Feature generation based on both variables education and marital-status seems to be promising.,582
adult_histograms.png,Feature generation based on both variables educational-num and marital-status seems to be promising.,583
adult_histograms.png,Feature generation based on both variables occupation and marital-status seems to be promising.,584
adult_histograms.png,Feature generation based on both variables relationship and marital-status seems to be promising.,585
adult_histograms.png,Feature generation based on both variables race and marital-status seems to be promising.,586
adult_histograms.png,Feature generation based on both variables gender and marital-status seems to be promising.,587
adult_histograms.png,Feature generation based on both variables capital-gain and marital-status seems to be promising.,588
adult_histograms.png,Feature generation based on both variables capital-loss and marital-status seems to be promising.,589
adult_histograms.png,Feature generation based on both variables hours-per-week and marital-status seems to be promising.,590
adult_histograms.png,Feature generation based on both variables age and occupation seems to be promising.,591
adult_histograms.png,Feature generation based on both variables workclass and occupation seems to be promising.,592
adult_histograms.png,Feature generation based on both variables fnlwgt and occupation seems to be promising.,593
adult_histograms.png,Feature generation based on both variables education and occupation seems to be promising.,594
adult_histograms.png,Feature generation based on both variables educational-num and occupation seems to be promising.,595
adult_histograms.png,Feature generation based on both variables marital-status and occupation seems to be promising.,596
adult_histograms.png,Feature generation based on both variables relationship and occupation seems to be promising.,597
adult_histograms.png,Feature generation based on both variables race and occupation seems to be promising.,598
adult_histograms.png,Feature generation based on both variables gender and occupation seems to be promising.,599
adult_histograms.png,Feature generation based on both variables capital-gain and occupation seems to be promising.,600
adult_histograms.png,Feature generation based on both variables capital-loss and occupation seems to be promising.,601
adult_histograms.png,Feature generation based on both variables hours-per-week and occupation seems to be promising.,602
adult_histograms.png,Feature generation based on both variables age and relationship seems to be promising.,603
adult_histograms.png,Feature generation based on both variables workclass and relationship seems to be promising.,604
adult_histograms.png,Feature generation based on both variables fnlwgt and relationship seems to be promising.,605
adult_histograms.png,Feature generation based on both variables education and relationship seems to be promising.,606
adult_histograms.png,Feature generation based on both variables educational-num and relationship seems to be promising.,607
adult_histograms.png,Feature generation based on both variables marital-status and relationship seems to be promising.,608
adult_histograms.png,Feature generation based on both variables occupation and relationship seems to be promising.,609
adult_histograms.png,Feature generation based on both variables race and relationship seems to be promising.,610
adult_histograms.png,Feature generation based on both variables gender and relationship seems to be promising.,611
adult_histograms.png,Feature generation based on both variables capital-gain and relationship seems to be promising.,612
adult_histograms.png,Feature generation based on both variables capital-loss and relationship seems to be promising.,613
adult_histograms.png,Feature generation based on both variables hours-per-week and relationship seems to be promising.,614
adult_histograms.png,Feature generation based on both variables age and race seems to be promising.,615
adult_histograms.png,Feature generation based on both variables workclass and race seems to be promising.,616
adult_histograms.png,Feature generation based on both variables fnlwgt and race seems to be promising.,617
adult_histograms.png,Feature generation based on both variables education and race seems to be promising.,618
adult_histograms.png,Feature generation based on both variables educational-num and race seems to be promising.,619
adult_histograms.png,Feature generation based on both variables marital-status and race seems to be promising.,620
adult_histograms.png,Feature generation based on both variables occupation and race seems to be promising.,621
adult_histograms.png,Feature generation based on both variables relationship and race seems to be promising.,622
adult_histograms.png,Feature generation based on both variables gender and race seems to be promising.,623
adult_histograms.png,Feature generation based on both variables capital-gain and race seems to be promising.,624
adult_histograms.png,Feature generation based on both variables capital-loss and race seems to be promising.,625
adult_histograms.png,Feature generation based on both variables hours-per-week and race seems to be promising.,626
adult_histograms.png,Feature generation based on both variables age and gender seems to be promising.,627
adult_histograms.png,Feature generation based on both variables workclass and gender seems to be promising.,628
adult_histograms.png,Feature generation based on both variables fnlwgt and gender seems to be promising.,629
adult_histograms.png,Feature generation based on both variables education and gender seems to be promising.,630
adult_histograms.png,Feature generation based on both variables educational-num and gender seems to be promising.,631
adult_histograms.png,Feature generation based on both variables marital-status and gender seems to be promising.,632
adult_histograms.png,Feature generation based on both variables occupation and gender seems to be promising.,633
adult_histograms.png,Feature generation based on both variables relationship and gender seems to be promising.,634
adult_histograms.png,Feature generation based on both variables race and gender seems to be promising.,635
adult_histograms.png,Feature generation based on both variables capital-gain and gender seems to be promising.,636
adult_histograms.png,Feature generation based on both variables capital-loss and gender seems to be promising.,637
adult_histograms.png,Feature generation based on both variables hours-per-week and gender seems to be promising.,638
adult_histograms.png,Feature generation based on both variables age and capital-gain seems to be promising.,639
adult_histograms.png,Feature generation based on both variables workclass and capital-gain seems to be promising.,640
adult_histograms.png,Feature generation based on both variables fnlwgt and capital-gain seems to be promising.,641
adult_histograms.png,Feature generation based on both variables education and capital-gain seems to be promising.,642
adult_histograms.png,Feature generation based on both variables educational-num and capital-gain seems to be promising.,643
adult_histograms.png,Feature generation based on both variables marital-status and capital-gain seems to be promising.,644
adult_histograms.png,Feature generation based on both variables occupation and capital-gain seems to be promising.,645
adult_histograms.png,Feature generation based on both variables relationship and capital-gain seems to be promising.,646
adult_histograms.png,Feature generation based on both variables race and capital-gain seems to be promising.,647
adult_histograms.png,Feature generation based on both variables gender and capital-gain seems to be promising.,648
adult_histograms.png,Feature generation based on both variables capital-loss and capital-gain seems to be promising.,649
adult_histograms.png,Feature generation based on both variables hours-per-week and capital-gain seems to be promising.,650
adult_histograms.png,Feature generation based on both variables age and capital-loss seems to be promising.,651
adult_histograms.png,Feature generation based on both variables workclass and capital-loss seems to be promising.,652
adult_histograms.png,Feature generation based on both variables fnlwgt and capital-loss seems to be promising.,653
adult_histograms.png,Feature generation based on both variables education and capital-loss seems to be promising.,654
adult_histograms.png,Feature generation based on both variables educational-num and capital-loss seems to be promising.,655
adult_histograms.png,Feature generation based on both variables marital-status and capital-loss seems to be promising.,656
adult_histograms.png,Feature generation based on both variables occupation and capital-loss seems to be promising.,657
adult_histograms.png,Feature generation based on both variables relationship and capital-loss seems to be promising.,658
adult_histograms.png,Feature generation based on both variables race and capital-loss seems to be promising.,659
adult_histograms.png,Feature generation based on both variables gender and capital-loss seems to be promising.,660
adult_histograms.png,Feature generation based on both variables capital-gain and capital-loss seems to be promising.,661
adult_histograms.png,Feature generation based on both variables hours-per-week and capital-loss seems to be promising.,662
adult_histograms.png,Feature generation based on both variables age and hours-per-week seems to be promising.,663
adult_histograms.png,Feature generation based on both variables workclass and hours-per-week seems to be promising.,664
adult_histograms.png,Feature generation based on both variables fnlwgt and hours-per-week seems to be promising.,665
adult_histograms.png,Feature generation based on both variables education and hours-per-week seems to be promising.,666
adult_histograms.png,Feature generation based on both variables educational-num and hours-per-week seems to be promising.,667
adult_histograms.png,Feature generation based on both variables marital-status and hours-per-week seems to be promising.,668
adult_histograms.png,Feature generation based on both variables occupation and hours-per-week seems to be promising.,669
adult_histograms.png,Feature generation based on both variables relationship and hours-per-week seems to be promising.,670
adult_histograms.png,Feature generation based on both variables race and hours-per-week seems to be promising.,671
adult_histograms.png,Feature generation based on both variables gender and hours-per-week seems to be promising.,672
adult_histograms.png,Feature generation based on both variables capital-gain and hours-per-week seems to be promising.,673
adult_histograms.png,Feature generation based on both variables capital-loss and hours-per-week seems to be promising.,674
adult_mv.png,There is no reason to believe that discarding records showing missing values is safer than discarding the corresponding variables in this case.,675
adult_mv.png,Dropping all rows with missing values can lead to a dataset with less than 25% of the original data.,676
adult_mv.png,Dropping all rows with missing values can lead to a dataset with less than 30% of the original data.,677
adult_mv.png,Dropping all rows with missing values can lead to a dataset with less than 40% of the original data.,678
adult_mv.png,Dropping all records with missing values would be better than to drop the variables with missing values.,679
adult_mv.png,Discarding variables workclass and age would be better than discarding all the records with missing values for those variables.,680
adult_mv.png,Discarding variables fnlwgt and age would be better than discarding all the records with missing values for those variables.,681
adult_mv.png,Discarding variables education and age would be better than discarding all the records with missing values for those variables.,682
adult_mv.png,Discarding variables educational-num and age would be better than discarding all the records with missing values for those variables.,683
adult_mv.png,Discarding variables marital-status and age would be better than discarding all the records with missing values for those variables.,684
adult_mv.png,Discarding variables occupation and age would be better than discarding all the records with missing values for those variables.,685
adult_mv.png,Discarding variables relationship and age would be better than discarding all the records with missing values for those variables.,686
adult_mv.png,Discarding variables race and age would be better than discarding all the records with missing values for those variables.,687
adult_mv.png,Discarding variables gender and age would be better than discarding all the records with missing values for those variables.,688
adult_mv.png,Discarding variables capital-gain and age would be better than discarding all the records with missing values for those variables.,689
adult_mv.png,Discarding variables capital-loss and age would be better than discarding all the records with missing values for those variables.,690
adult_mv.png,Discarding variables hours-per-week and age would be better than discarding all the records with missing values for those variables.,691
adult_mv.png,Discarding variables age and workclass would be better than discarding all the records with missing values for those variables.,692
adult_mv.png,Discarding variables fnlwgt and workclass would be better than discarding all the records with missing values for those variables.,693
adult_mv.png,Discarding variables education and workclass would be better than discarding all the records with missing values for those variables.,694
adult_mv.png,Discarding variables educational-num and workclass would be better than discarding all the records with missing values for those variables.,695
adult_mv.png,Discarding variables marital-status and workclass would be better than discarding all the records with missing values for those variables.,696
adult_mv.png,Discarding variables occupation and workclass would be better than discarding all the records with missing values for those variables.,697
adult_mv.png,Discarding variables relationship and workclass would be better than discarding all the records with missing values for those variables.,698
adult_mv.png,Discarding variables race and workclass would be better than discarding all the records with missing values for those variables.,699
adult_mv.png,Discarding variables gender and workclass would be better than discarding all the records with missing values for those variables.,700
adult_mv.png,Discarding variables capital-gain and workclass would be better than discarding all the records with missing values for those variables.,701
adult_mv.png,Discarding variables capital-loss and workclass would be better than discarding all the records with missing values for those variables.,702
adult_mv.png,Discarding variables hours-per-week and workclass would be better than discarding all the records with missing values for those variables.,703
adult_mv.png,Discarding variables age and fnlwgt would be better than discarding all the records with missing values for those variables.,704
adult_mv.png,Discarding variables workclass and fnlwgt would be better than discarding all the records with missing values for those variables.,705
adult_mv.png,Discarding variables education and fnlwgt would be better than discarding all the records with missing values for those variables.,706
adult_mv.png,Discarding variables educational-num and fnlwgt would be better than discarding all the records with missing values for those variables.,707
adult_mv.png,Discarding variables marital-status and fnlwgt would be better than discarding all the records with missing values for those variables.,708
adult_mv.png,Discarding variables occupation and fnlwgt would be better than discarding all the records with missing values for those variables.,709
adult_mv.png,Discarding variables relationship and fnlwgt would be better than discarding all the records with missing values for those variables.,710
adult_mv.png,Discarding variables race and fnlwgt would be better than discarding all the records with missing values for those variables.,711
adult_mv.png,Discarding variables gender and fnlwgt would be better than discarding all the records with missing values for those variables.,712
adult_mv.png,Discarding variables capital-gain and fnlwgt would be better than discarding all the records with missing values for those variables.,713
adult_mv.png,Discarding variables capital-loss and fnlwgt would be better than discarding all the records with missing values for those variables.,714
adult_mv.png,Discarding variables hours-per-week and fnlwgt would be better than discarding all the records with missing values for those variables.,715
adult_mv.png,Discarding variables age and education would be better than discarding all the records with missing values for those variables.,716
adult_mv.png,Discarding variables workclass and education would be better than discarding all the records with missing values for those variables.,717
adult_mv.png,Discarding variables fnlwgt and education would be better than discarding all the records with missing values for those variables.,718
adult_mv.png,Discarding variables marital-status and education would be better than discarding all the records with missing values for those variables.,719
adult_mv.png,Discarding variables occupation and education would be better than discarding all the records with missing values for those variables.,720
adult_mv.png,Discarding variables relationship and education would be better than discarding all the records with missing values for those variables.,721
adult_mv.png,Discarding variables race and education would be better than discarding all the records with missing values for those variables.,722
adult_mv.png,Discarding variables gender and education would be better than discarding all the records with missing values for those variables.,723
adult_mv.png,Discarding variables capital-gain and education would be better than discarding all the records with missing values for those variables.,724
adult_mv.png,Discarding variables capital-loss and education would be better than discarding all the records with missing values for those variables.,725
adult_mv.png,Discarding variables hours-per-week and education would be better than discarding all the records with missing values for those variables.,726
adult_mv.png,Discarding variables age and educational-num would be better than discarding all the records with missing values for those variables.,727
adult_mv.png,Discarding variables workclass and educational-num would be better than discarding all the records with missing values for those variables.,728
adult_mv.png,Discarding variables fnlwgt and educational-num would be better than discarding all the records with missing values for those variables.,729
adult_mv.png,Discarding variables education and educational-num would be better than discarding all the records with missing values for those variables.,730
adult_mv.png,Discarding variables marital-status and educational-num would be better than discarding all the records with missing values for those variables.,731
adult_mv.png,Discarding variables occupation and educational-num would be better than discarding all the records with missing values for those variables.,732
adult_mv.png,Discarding variables relationship and educational-num would be better than discarding all the records with missing values for those variables.,733
adult_mv.png,Discarding variables race and educational-num would be better than discarding all the records with missing values for those variables.,734
adult_mv.png,Discarding variables gender and educational-num would be better than discarding all the records with missing values for those variables.,735
adult_mv.png,Discarding variables capital-gain and educational-num would be better than discarding all the records with missing values for those variables.,736
adult_mv.png,Discarding variables capital-loss and educational-num would be better than discarding all the records with missing values for those variables.,737
adult_mv.png,Discarding variables hours-per-week and educational-num would be better than discarding all the records with missing values for those variables.,738
adult_mv.png,Discarding variables age and marital-status would be better than discarding all the records with missing values for those variables.,739
adult_mv.png,Discarding variables workclass and marital-status would be better than discarding all the records with missing values for those variables.,740
adult_mv.png,Discarding variables fnlwgt and marital-status would be better than discarding all the records with missing values for those variables.,741
adult_mv.png,Discarding variables education and marital-status would be better than discarding all the records with missing values for those variables.,742
adult_mv.png,Discarding variables educational-num and marital-status would be better than discarding all the records with missing values for those variables.,743
adult_mv.png,Discarding variables occupation and marital-status would be better than discarding all the records with missing values for those variables.,744
adult_mv.png,Discarding variables relationship and marital-status would be better than discarding all the records with missing values for those variables.,745
adult_mv.png,Discarding variables race and marital-status would be better than discarding all the records with missing values for those variables.,746
adult_mv.png,Discarding variables gender and marital-status would be better than discarding all the records with missing values for those variables.,747
adult_mv.png,Discarding variables capital-gain and marital-status would be better than discarding all the records with missing values for those variables.,748
adult_mv.png,Discarding variables capital-loss and marital-status would be better than discarding all the records with missing values for those variables.,749
adult_mv.png,Discarding variables hours-per-week and marital-status would be better than discarding all the records with missing values for those variables.,750
adult_mv.png,Discarding variables age and occupation would be better than discarding all the records with missing values for those variables.,751
adult_mv.png,Discarding variables workclass and occupation would be better than discarding all the records with missing values for those variables.,752
adult_mv.png,Discarding variables fnlwgt and occupation would be better than discarding all the records with missing values for those variables.,753
adult_mv.png,Discarding variables education and occupation would be better than discarding all the records with missing values for those variables.,754
adult_mv.png,Discarding variables educational-num and occupation would be better than discarding all the records with missing values for those variables.,755
adult_mv.png,Discarding variables marital-status and occupation would be better than discarding all the records with missing values for those variables.,756
adult_mv.png,Discarding variables relationship and occupation would be better than discarding all the records with missing values for those variables.,757
adult_mv.png,Discarding variables race and occupation would be better than discarding all the records with missing values for those variables.,758
adult_mv.png,Discarding variables gender and occupation would be better than discarding all the records with missing values for those variables.,759
adult_mv.png,Discarding variables capital-gain and occupation would be better than discarding all the records with missing values for those variables.,760
adult_mv.png,Discarding variables capital-loss and occupation would be better than discarding all the records with missing values for those variables.,761
adult_mv.png,Discarding variables hours-per-week and occupation would be better than discarding all the records with missing values for those variables.,762
adult_mv.png,Discarding variables age and relationship would be better than discarding all the records with missing values for those variables.,763
adult_mv.png,Discarding variables workclass and relationship would be better than discarding all the records with missing values for those variables.,764
adult_mv.png,Discarding variables fnlwgt and relationship would be better than discarding all the records with missing values for those variables.,765
adult_mv.png,Discarding variables education and relationship would be better than discarding all the records with missing values for those variables.,766
adult_mv.png,Discarding variables educational-num and relationship would be better than discarding all the records with missing values for those variables.,767
adult_mv.png,Discarding variables marital-status and relationship would be better than discarding all the records with missing values for those variables.,768
adult_mv.png,Discarding variables occupation and relationship would be better than discarding all the records with missing values for those variables.,769
adult_mv.png,Discarding variables race and relationship would be better than discarding all the records with missing values for those variables.,770
adult_mv.png,Discarding variables gender and relationship would be better than discarding all the records with missing values for those variables.,771
adult_mv.png,Discarding variables capital-gain and relationship would be better than discarding all the records with missing values for those variables.,772
adult_mv.png,Discarding variables capital-loss and relationship would be better than discarding all the records with missing values for those variables.,773
adult_mv.png,Discarding variables hours-per-week and relationship would be better than discarding all the records with missing values for those variables.,774
adult_mv.png,Discarding variables age and race would be better than discarding all the records with missing values for those variables.,775
adult_mv.png,Discarding variables workclass and race would be better than discarding all the records with missing values for those variables.,776
adult_mv.png,Discarding variables fnlwgt and race would be better than discarding all the records with missing values for those variables.,777
adult_mv.png,Discarding variables education and race would be better than discarding all the records with missing values for those variables.,778
adult_mv.png,Discarding variables educational-num and race would be better than discarding all the records with missing values for those variables.,779
adult_mv.png,Discarding variables marital-status and race would be better than discarding all the records with missing values for those variables.,780
adult_mv.png,Discarding variables occupation and race would be better than discarding all the records with missing values for those variables.,781
adult_mv.png,Discarding variables relationship and race would be better than discarding all the records with missing values for those variables.,782
adult_mv.png,Discarding variables gender and race would be better than discarding all the records with missing values for those variables.,783
adult_mv.png,Discarding variables capital-gain and race would be better than discarding all the records with missing values for those variables.,784
adult_mv.png,Discarding variables capital-loss and race would be better than discarding all the records with missing values for those variables.,785
adult_mv.png,Discarding variables hours-per-week and race would be better than discarding all the records with missing values for those variables.,786
adult_mv.png,Discarding variables age and gender would be better than discarding all the records with missing values for those variables.,787
adult_mv.png,Discarding variables workclass and gender would be better than discarding all the records with missing values for those variables.,788
adult_mv.png,Discarding variables fnlwgt and gender would be better than discarding all the records with missing values for those variables.,789
adult_mv.png,Discarding variables education and gender would be better than discarding all the records with missing values for those variables.,790
adult_mv.png,Discarding variables educational-num and gender would be better than discarding all the records with missing values for those variables.,791
adult_mv.png,Discarding variables marital-status and gender would be better than discarding all the records with missing values for those variables.,792
adult_mv.png,Discarding variables occupation and gender would be better than discarding all the records with missing values for those variables.,793
adult_mv.png,Discarding variables relationship and gender would be better than discarding all the records with missing values for those variables.,794
adult_mv.png,Discarding variables race and gender would be better than discarding all the records with missing values for those variables.,795
adult_mv.png,Discarding variables capital-gain and gender would be better than discarding all the records with missing values for those variables.,796
adult_mv.png,Discarding variables capital-loss and gender would be better than discarding all the records with missing values for those variables.,797
adult_mv.png,Discarding variables hours-per-week and gender would be better than discarding all the records with missing values for those variables.,798
adult_mv.png,Discarding variables age and capital-gain would be better than discarding all the records with missing values for those variables.,799
adult_mv.png,Discarding variables workclass and capital-gain would be better than discarding all the records with missing values for those variables.,800
adult_mv.png,Discarding variables fnlwgt and capital-gain would be better than discarding all the records with missing values for those variables.,801
adult_mv.png,Discarding variables education and capital-gain would be better than discarding all the records with missing values for those variables.,802
adult_mv.png,Discarding variables educational-num and capital-gain would be better than discarding all the records with missing values for those variables.,803
adult_mv.png,Discarding variables marital-status and capital-gain would be better than discarding all the records with missing values for those variables.,804
adult_mv.png,Discarding variables occupation and capital-gain would be better than discarding all the records with missing values for those variables.,805
adult_mv.png,Discarding variables relationship and capital-gain would be better than discarding all the records with missing values for those variables.,806
adult_mv.png,Discarding variables race and capital-gain would be better than discarding all the records with missing values for those variables.,807
adult_mv.png,Discarding variables gender and capital-gain would be better than discarding all the records with missing values for those variables.,808
adult_mv.png,Discarding variables capital-loss and capital-gain would be better than discarding all the records with missing values for those variables.,809
adult_mv.png,Discarding variables hours-per-week and capital-gain would be better than discarding all the records with missing values for those variables.,810
adult_mv.png,Discarding variables age and capital-loss would be better than discarding all the records with missing values for those variables.,811
adult_mv.png,Discarding variables workclass and capital-loss would be better than discarding all the records with missing values for those variables.,812
adult_mv.png,Discarding variables fnlwgt and capital-loss would be better than discarding all the records with missing values for those variables.,813
adult_mv.png,Discarding variables education and capital-loss would be better than discarding all the records with missing values for those variables.,814
adult_mv.png,Discarding variables educational-num and capital-loss would be better than discarding all the records with missing values for those variables.,815
adult_mv.png,Discarding variables marital-status and capital-loss would be better than discarding all the records with missing values for those variables.,816
adult_mv.png,Discarding variables occupation and capital-loss would be better than discarding all the records with missing values for those variables.,817
adult_mv.png,Discarding variables relationship and capital-loss would be better than discarding all the records with missing values for those variables.,818
adult_mv.png,Discarding variables race and capital-loss would be better than discarding all the records with missing values for those variables.,819
adult_mv.png,Discarding variables gender and capital-loss would be better than discarding all the records with missing values for those variables.,820
adult_mv.png,Discarding variables capital-gain and capital-loss would be better than discarding all the records with missing values for those variables.,821
adult_mv.png,Discarding variables hours-per-week and capital-loss would be better than discarding all the records with missing values for those variables.,822
adult_mv.png,Discarding variables age and hours-per-week would be better than discarding all the records with missing values for those variables.,823
adult_mv.png,Discarding variables workclass and hours-per-week would be better than discarding all the records with missing values for those variables.,824
adult_mv.png,Discarding variables fnlwgt and hours-per-week would be better than discarding all the records with missing values for those variables.,825
adult_mv.png,Discarding variables education and hours-per-week would be better than discarding all the records with missing values for those variables.,826
adult_mv.png,Discarding variables educational-num and hours-per-week would be better than discarding all the records with missing values for those variables.,827
adult_mv.png,Discarding variables marital-status and hours-per-week would be better than discarding all the records with missing values for those variables.,828
adult_mv.png,Discarding variables occupation and hours-per-week would be better than discarding all the records with missing values for those variables.,829
adult_mv.png,Discarding variables relationship and hours-per-week would be better than discarding all the records with missing values for those variables.,830
adult_mv.png,Discarding variables race and hours-per-week would be better than discarding all the records with missing values for those variables.,831
adult_mv.png,Discarding variables gender and hours-per-week would be better than discarding all the records with missing values for those variables.,832
adult_mv.png,Discarding variables capital-gain and hours-per-week would be better than discarding all the records with missing values for those variables.,833
adult_mv.png,Discarding variables capital-loss and hours-per-week would be better than discarding all the records with missing values for those variables.,834
adult_histograms.png,The variable age can be coded as ordinal without losing information.,835
adult_histograms.png,The variable workclass can be coded as ordinal without losing information.,836
adult_histograms.png,The variable fnlwgt can be coded as ordinal without losing information.,837
adult_histograms.png,The variable education can be coded as ordinal without losing information.,838
adult_histograms.png,The variable educational-num can be coded as ordinal without losing information.,839
adult_histograms.png,The variable marital-status can be coded as ordinal without losing information.,840
adult_histograms.png,The variable occupation can be coded as ordinal without losing information.,841
adult_histograms.png,The variable relationship can be coded as ordinal without losing information.,842
adult_histograms.png,The variable race can be coded as ordinal without losing information.,843
adult_histograms.png,The variable gender can be coded as ordinal without losing information.,844
adult_histograms.png,The variable capital-gain can be coded as ordinal without losing information.,845
adult_histograms.png,The variable capital-loss can be coded as ordinal without losing information.,846
adult_histograms.png,The variable hours-per-week can be coded as ordinal without losing information.,847
adult_histograms.png,"Considering the common semantics for age variable, dummification would be the most adequate encoding.",848
adult_histograms.png,"Considering the common semantics for workclass variable, dummification would be the most adequate encoding.",849
adult_histograms.png,"Considering the common semantics for fnlwgt variable, dummification would be the most adequate encoding.",850
adult_histograms.png,"Considering the common semantics for education variable, dummification would be the most adequate encoding.",851
adult_histograms.png,"Considering the common semantics for educational-num variable, dummification would be the most adequate encoding.",852
adult_histograms.png,"Considering the common semantics for marital-status variable, dummification would be the most adequate encoding.",853
adult_histograms.png,"Considering the common semantics for occupation variable, dummification would be the most adequate encoding.",854
adult_histograms.png,"Considering the common semantics for relationship variable, dummification would be the most adequate encoding.",855
adult_histograms.png,"Considering the common semantics for race variable, dummification would be the most adequate encoding.",856
adult_histograms.png,"Considering the common semantics for gender variable, dummification would be the most adequate encoding.",857
adult_histograms.png,"Considering the common semantics for capital-gain variable, dummification would be the most adequate encoding.",858
adult_histograms.png,"Considering the common semantics for capital-loss variable, dummification would be the most adequate encoding.",859
adult_histograms.png,"Considering the common semantics for hours-per-week variable, dummification would be the most adequate encoding.",860
adult_histograms.png,"Considering the common semantics for workclass and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",861
adult_histograms.png,"Considering the common semantics for fnlwgt and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",862
adult_histograms.png,"Considering the common semantics for education and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",863
adult_histograms.png,"Considering the common semantics for educational-num and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",864
adult_histograms.png,"Considering the common semantics for marital-status and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",865
adult_histograms.png,"Considering the common semantics for occupation and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",866
adult_histograms.png,"Considering the common semantics for relationship and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",867
adult_histograms.png,"Considering the common semantics for race and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",868
adult_histograms.png,"Considering the common semantics for gender and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",869
adult_histograms.png,"Considering the common semantics for capital-gain and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",870
adult_histograms.png,"Considering the common semantics for capital-loss and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",871
adult_histograms.png,"Considering the common semantics for hours-per-week and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",872
adult_histograms.png,"Considering the common semantics for age and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",873
adult_histograms.png,"Considering the common semantics for fnlwgt and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",874
adult_histograms.png,"Considering the common semantics for education and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",875
adult_histograms.png,"Considering the common semantics for educational-num and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",876
adult_histograms.png,"Considering the common semantics for marital-status and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",877
adult_histograms.png,"Considering the common semantics for occupation and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",878
adult_histograms.png,"Considering the common semantics for relationship and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",879
adult_histograms.png,"Considering the common semantics for race and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",880
adult_histograms.png,"Considering the common semantics for gender and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",881
adult_histograms.png,"Considering the common semantics for capital-gain and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",882
adult_histograms.png,"Considering the common semantics for capital-loss and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",883
adult_histograms.png,"Considering the common semantics for hours-per-week and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",884
adult_histograms.png,"Considering the common semantics for age and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",885
adult_histograms.png,"Considering the common semantics for workclass and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",886
adult_histograms.png,"Considering the common semantics for education and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",887
adult_histograms.png,"Considering the common semantics for educational-num and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",888
adult_histograms.png,"Considering the common semantics for marital-status and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",889
adult_histograms.png,"Considering the common semantics for occupation and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",890
adult_histograms.png,"Considering the common semantics for relationship and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",891
adult_histograms.png,"Considering the common semantics for race and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",892
adult_histograms.png,"Considering the common semantics for gender and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",893
adult_histograms.png,"Considering the common semantics for capital-gain and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",894
adult_histograms.png,"Considering the common semantics for capital-loss and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",895
adult_histograms.png,"Considering the common semantics for hours-per-week and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",896
adult_histograms.png,"Considering the common semantics for age and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",897
adult_histograms.png,"Considering the common semantics for workclass and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",898
adult_histograms.png,"Considering the common semantics for fnlwgt and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",899
adult_histograms.png,"Considering the common semantics for marital-status and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",900
adult_histograms.png,"Considering the common semantics for occupation and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",901
adult_histograms.png,"Considering the common semantics for relationship and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",902
adult_histograms.png,"Considering the common semantics for race and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",903
adult_histograms.png,"Considering the common semantics for gender and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",904
adult_histograms.png,"Considering the common semantics for capital-gain and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",905
adult_histograms.png,"Considering the common semantics for capital-loss and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",906
adult_histograms.png,"Considering the common semantics for hours-per-week and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",907
adult_histograms.png,"Considering the common semantics for age and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",908
adult_histograms.png,"Considering the common semantics for workclass and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",909
adult_histograms.png,"Considering the common semantics for fnlwgt and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",910
adult_histograms.png,"Considering the common semantics for education and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",911
adult_histograms.png,"Considering the common semantics for marital-status and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",912
adult_histograms.png,"Considering the common semantics for occupation and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",913
adult_histograms.png,"Considering the common semantics for relationship and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",914
adult_histograms.png,"Considering the common semantics for race and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",915
adult_histograms.png,"Considering the common semantics for gender and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",916
adult_histograms.png,"Considering the common semantics for capital-gain and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",917
adult_histograms.png,"Considering the common semantics for capital-loss and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",918
adult_histograms.png,"Considering the common semantics for hours-per-week and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",919
adult_histograms.png,"Considering the common semantics for age and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",920
adult_histograms.png,"Considering the common semantics for workclass and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",921
adult_histograms.png,"Considering the common semantics for fnlwgt and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",922
adult_histograms.png,"Considering the common semantics for education and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",923
adult_histograms.png,"Considering the common semantics for educational-num and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",924
adult_histograms.png,"Considering the common semantics for occupation and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",925
adult_histograms.png,"Considering the common semantics for relationship and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",926
adult_histograms.png,"Considering the common semantics for race and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",927
adult_histograms.png,"Considering the common semantics for gender and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",928
adult_histograms.png,"Considering the common semantics for capital-gain and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",929
adult_histograms.png,"Considering the common semantics for capital-loss and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",930
adult_histograms.png,"Considering the common semantics for hours-per-week and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",931
adult_histograms.png,"Considering the common semantics for age and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",932
adult_histograms.png,"Considering the common semantics for workclass and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",933
adult_histograms.png,"Considering the common semantics for fnlwgt and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",934
adult_histograms.png,"Considering the common semantics for education and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",935
adult_histograms.png,"Considering the common semantics for educational-num and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",936
adult_histograms.png,"Considering the common semantics for marital-status and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",937
adult_histograms.png,"Considering the common semantics for relationship and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",938
adult_histograms.png,"Considering the common semantics for race and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",939
adult_histograms.png,"Considering the common semantics for gender and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",940
adult_histograms.png,"Considering the common semantics for capital-gain and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",941
adult_histograms.png,"Considering the common semantics for capital-loss and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",942
adult_histograms.png,"Considering the common semantics for hours-per-week and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",943
adult_histograms.png,"Considering the common semantics for age and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",944
adult_histograms.png,"Considering the common semantics for workclass and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",945
adult_histograms.png,"Considering the common semantics for fnlwgt and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",946
adult_histograms.png,"Considering the common semantics for education and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",947
adult_histograms.png,"Considering the common semantics for educational-num and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",948
adult_histograms.png,"Considering the common semantics for marital-status and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",949
adult_histograms.png,"Considering the common semantics for occupation and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",950
adult_histograms.png,"Considering the common semantics for race and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",951
adult_histograms.png,"Considering the common semantics for gender and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",952
adult_histograms.png,"Considering the common semantics for capital-gain and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",953
adult_histograms.png,"Considering the common semantics for capital-loss and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",954
adult_histograms.png,"Considering the common semantics for hours-per-week and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",955
adult_histograms.png,"Considering the common semantics for age and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",956
adult_histograms.png,"Considering the common semantics for workclass and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",957
adult_histograms.png,"Considering the common semantics for fnlwgt and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",958
adult_histograms.png,"Considering the common semantics for education and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",959
adult_histograms.png,"Considering the common semantics for educational-num and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",960
adult_histograms.png,"Considering the common semantics for marital-status and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",961
adult_histograms.png,"Considering the common semantics for occupation and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",962
adult_histograms.png,"Considering the common semantics for relationship and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",963
adult_histograms.png,"Considering the common semantics for gender and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",964
adult_histograms.png,"Considering the common semantics for capital-gain and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",965
adult_histograms.png,"Considering the common semantics for capital-loss and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",966
adult_histograms.png,"Considering the common semantics for hours-per-week and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",967
adult_histograms.png,"Considering the common semantics for age and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",968
adult_histograms.png,"Considering the common semantics for workclass and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",969
adult_histograms.png,"Considering the common semantics for fnlwgt and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",970
adult_histograms.png,"Considering the common semantics for education and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",971
adult_histograms.png,"Considering the common semantics for educational-num and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",972
adult_histograms.png,"Considering the common semantics for marital-status and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",973
adult_histograms.png,"Considering the common semantics for occupation and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",974
adult_histograms.png,"Considering the common semantics for relationship and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",975
adult_histograms.png,"Considering the common semantics for race and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",976
adult_histograms.png,"Considering the common semantics for capital-gain and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",977
adult_histograms.png,"Considering the common semantics for capital-loss and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",978
adult_histograms.png,"Considering the common semantics for hours-per-week and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",979
adult_histograms.png,"Considering the common semantics for age and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",980
adult_histograms.png,"Considering the common semantics for workclass and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",981
adult_histograms.png,"Considering the common semantics for fnlwgt and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",982
adult_histograms.png,"Considering the common semantics for education and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",983
adult_histograms.png,"Considering the common semantics for educational-num and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",984
adult_histograms.png,"Considering the common semantics for marital-status and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",985
adult_histograms.png,"Considering the common semantics for occupation and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",986
adult_histograms.png,"Considering the common semantics for relationship and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",987
adult_histograms.png,"Considering the common semantics for race and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",988
adult_histograms.png,"Considering the common semantics for gender and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",989
adult_histograms.png,"Considering the common semantics for capital-loss and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",990
adult_histograms.png,"Considering the common semantics for hours-per-week and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",991
adult_histograms.png,"Considering the common semantics for age and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",992
adult_histograms.png,"Considering the common semantics for workclass and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",993
adult_histograms.png,"Considering the common semantics for fnlwgt and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",994
adult_histograms.png,"Considering the common semantics for education and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",995
adult_histograms.png,"Considering the common semantics for educational-num and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",996
adult_histograms.png,"Considering the common semantics for marital-status and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",997
adult_histograms.png,"Considering the common semantics for occupation and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",998
adult_histograms.png,"Considering the common semantics for relationship and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",999
adult_histograms.png,"Considering the common semantics for race and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1000
adult_histograms.png,"Considering the common semantics for gender and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1001
adult_histograms.png,"Considering the common semantics for capital-gain and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1002
adult_histograms.png,"Considering the common semantics for hours-per-week and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1003
adult_histograms.png,"Considering the common semantics for age and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1004
adult_histograms.png,"Considering the common semantics for workclass and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1005
adult_histograms.png,"Considering the common semantics for fnlwgt and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1006
adult_histograms.png,"Considering the common semantics for education and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1007
adult_histograms.png,"Considering the common semantics for educational-num and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1008
adult_histograms.png,"Considering the common semantics for marital-status and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1009
adult_histograms.png,"Considering the common semantics for occupation and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1010
adult_histograms.png,"Considering the common semantics for relationship and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1011
adult_histograms.png,"Considering the common semantics for race and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1012
adult_histograms.png,"Considering the common semantics for gender and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1013
adult_histograms.png,"Considering the common semantics for capital-gain and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1014
adult_histograms.png,"Considering the common semantics for capital-loss and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1015
adult_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,1016
adult_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,1017
adult_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,1018
adult_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",1019
adult_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",1020
adult_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",1021
adult_correlation_heatmap.png,Variables fnlwgt and age seem to be useful for classification tasks.,1022
adult_correlation_heatmap.png,Variables educational-num and age seem to be useful for classification tasks.,1023
adult_correlation_heatmap.png,Variables capital-gain and age seem to be useful for classification tasks.,1024
adult_correlation_heatmap.png,Variables capital-loss and age seem to be useful for classification tasks.,1025
adult_correlation_heatmap.png,Variables hours-per-week and age seem to be useful for classification tasks.,1026
adult_correlation_heatmap.png,Variables age and fnlwgt seem to be useful for classification tasks.,1027
adult_correlation_heatmap.png,Variables educational-num and fnlwgt seem to be useful for classification tasks.,1028
adult_correlation_heatmap.png,Variables capital-gain and fnlwgt seem to be useful for classification tasks.,1029
adult_correlation_heatmap.png,Variables capital-loss and fnlwgt seem to be useful for classification tasks.,1030
adult_correlation_heatmap.png,Variables hours-per-week and fnlwgt seem to be useful for classification tasks.,1031
adult_correlation_heatmap.png,Variables age and educational-num seem to be useful for classification tasks.,1032
adult_correlation_heatmap.png,Variables fnlwgt and educational-num seem to be useful for classification tasks.,1033
adult_correlation_heatmap.png,Variables capital-gain and educational-num seem to be useful for classification tasks.,1034
adult_correlation_heatmap.png,Variables capital-loss and educational-num seem to be useful for classification tasks.,1035
adult_correlation_heatmap.png,Variables hours-per-week and educational-num seem to be useful for classification tasks.,1036
adult_correlation_heatmap.png,Variables age and capital-gain seem to be useful for classification tasks.,1037
adult_correlation_heatmap.png,Variables fnlwgt and capital-gain seem to be useful for classification tasks.,1038
adult_correlation_heatmap.png,Variables educational-num and capital-gain seem to be useful for classification tasks.,1039
adult_correlation_heatmap.png,Variables capital-loss and capital-gain seem to be useful for classification tasks.,1040
adult_correlation_heatmap.png,Variables hours-per-week and capital-gain seem to be useful for classification tasks.,1041
adult_correlation_heatmap.png,Variables age and capital-loss seem to be useful for classification tasks.,1042
adult_correlation_heatmap.png,Variables fnlwgt and capital-loss seem to be useful for classification tasks.,1043
adult_correlation_heatmap.png,Variables educational-num and capital-loss seem to be useful for classification tasks.,1044
adult_correlation_heatmap.png,Variables capital-gain and capital-loss seem to be useful for classification tasks.,1045
adult_correlation_heatmap.png,Variables hours-per-week and capital-loss seem to be useful for classification tasks.,1046
adult_correlation_heatmap.png,Variables age and hours-per-week seem to be useful for classification tasks.,1047
adult_correlation_heatmap.png,Variables fnlwgt and hours-per-week seem to be useful for classification tasks.,1048
adult_correlation_heatmap.png,Variables educational-num and hours-per-week seem to be useful for classification tasks.,1049
adult_correlation_heatmap.png,Variables capital-gain and hours-per-week seem to be useful for classification tasks.,1050
adult_correlation_heatmap.png,Variables capital-loss and hours-per-week seem to be useful for classification tasks.,1051
adult_correlation_heatmap.png,Variable age seems to be relevant for the majority of mining tasks.,1052
adult_correlation_heatmap.png,Variable fnlwgt seems to be relevant for the majority of mining tasks.,1053
adult_correlation_heatmap.png,Variable educational-num seems to be relevant for the majority of mining tasks.,1054
adult_correlation_heatmap.png,Variable capital-gain seems to be relevant for the majority of mining tasks.,1055
adult_correlation_heatmap.png,Variable capital-loss seems to be relevant for the majority of mining tasks.,1056
adult_correlation_heatmap.png,Variable hours-per-week seems to be relevant for the majority of mining tasks.,1057
adult_decision_tree.png,Variable age is one of the most relevant variables.,1058
adult_decision_tree.png,Variable fnlwgt is one of the most relevant variables.,1059
adult_decision_tree.png,Variable educational-num is one of the most relevant variables.,1060
adult_decision_tree.png,Variable capital-gain is one of the most relevant variables.,1061
adult_decision_tree.png,Variable capital-loss is one of the most relevant variables.,1062
adult_decision_tree.png,Variable hours-per-week is one of the most relevant variables.,1063
adult_decision_tree.png,It is possible to state that age is the first most discriminative variable regarding the class.,1064
adult_decision_tree.png,It is possible to state that fnlwgt is the first most discriminative variable regarding the class.,1065
adult_decision_tree.png,It is possible to state that educational-num is the first most discriminative variable regarding the class.,1066
adult_decision_tree.png,It is possible to state that capital-gain is the first most discriminative variable regarding the class.,1067
adult_decision_tree.png,It is possible to state that capital-loss is the first most discriminative variable regarding the class.,1068
adult_decision_tree.png,It is possible to state that hours-per-week is the first most discriminative variable regarding the class.,1069
adult_decision_tree.png,It is possible to state that age is the second most discriminative variable regarding the class.,1070
adult_decision_tree.png,It is possible to state that fnlwgt is the second most discriminative variable regarding the class.,1071
adult_decision_tree.png,It is possible to state that educational-num is the second most discriminative variable regarding the class.,1072
adult_decision_tree.png,It is possible to state that capital-gain is the second most discriminative variable regarding the class.,1073
adult_decision_tree.png,It is possible to state that capital-loss is the second most discriminative variable regarding the class.,1074
adult_decision_tree.png,It is possible to state that hours-per-week is the second most discriminative variable regarding the class.,1075
adult_decision_tree.png,"The variable age discriminates between the target values, as shown in the decision tree.",1076
adult_decision_tree.png,"The variable fnlwgt discriminates between the target values, as shown in the decision tree.",1077
adult_decision_tree.png,"The variable educational-num discriminates between the target values, as shown in the decision tree.",1078
adult_decision_tree.png,"The variable capital-gain discriminates between the target values, as shown in the decision tree.",1079
adult_decision_tree.png,"The variable capital-loss discriminates between the target values, as shown in the decision tree.",1080
adult_decision_tree.png,"The variable hours-per-week discriminates between the target values, as shown in the decision tree.",1081
adult_decision_tree.png,The variable age seems to be one of the two most relevant features.,1082
adult_decision_tree.png,The variable fnlwgt seems to be one of the two most relevant features.,1083
adult_decision_tree.png,The variable educational-num seems to be one of the two most relevant features.,1084
adult_decision_tree.png,The variable capital-gain seems to be one of the two most relevant features.,1085
adult_decision_tree.png,The variable capital-loss seems to be one of the two most relevant features.,1086
adult_decision_tree.png,The variable hours-per-week seems to be one of the two most relevant features.,1087
adult_decision_tree.png,The variable age seems to be one of the three most relevant features.,1088
adult_decision_tree.png,The variable fnlwgt seems to be one of the three most relevant features.,1089
adult_decision_tree.png,The variable educational-num seems to be one of the three most relevant features.,1090
adult_decision_tree.png,The variable capital-gain seems to be one of the three most relevant features.,1091
adult_decision_tree.png,The variable capital-loss seems to be one of the three most relevant features.,1092
adult_decision_tree.png,The variable hours-per-week seems to be one of the three most relevant features.,1093
adult_decision_tree.png,The variable age seems to be one of the four most relevant features.,1094
adult_decision_tree.png,The variable fnlwgt seems to be one of the four most relevant features.,1095
adult_decision_tree.png,The variable educational-num seems to be one of the four most relevant features.,1096
adult_decision_tree.png,The variable capital-gain seems to be one of the four most relevant features.,1097
adult_decision_tree.png,The variable capital-loss seems to be one of the four most relevant features.,1098
adult_decision_tree.png,The variable hours-per-week seems to be one of the four most relevant features.,1099
adult_decision_tree.png,The variable age seems to be one of the five most relevant features.,1100
adult_decision_tree.png,The variable fnlwgt seems to be one of the five most relevant features.,1101
adult_decision_tree.png,The variable educational-num seems to be one of the five most relevant features.,1102
adult_decision_tree.png,The variable capital-gain seems to be one of the five most relevant features.,1103
adult_decision_tree.png,The variable capital-loss seems to be one of the five most relevant features.,1104
adult_decision_tree.png,The variable hours-per-week seems to be one of the five most relevant features.,1105
adult_decision_tree.png,It is clear that variable age is one of the two most relevant features.,1106
adult_decision_tree.png,It is clear that variable fnlwgt is one of the two most relevant features.,1107
adult_decision_tree.png,It is clear that variable educational-num is one of the two most relevant features.,1108
adult_decision_tree.png,It is clear that variable capital-gain is one of the two most relevant features.,1109
adult_decision_tree.png,It is clear that variable capital-loss is one of the two most relevant features.,1110
adult_decision_tree.png,It is clear that variable hours-per-week is one of the two most relevant features.,1111
adult_decision_tree.png,It is clear that variable age is one of the three most relevant features.,1112
adult_decision_tree.png,It is clear that variable fnlwgt is one of the three most relevant features.,1113
adult_decision_tree.png,It is clear that variable educational-num is one of the three most relevant features.,1114
adult_decision_tree.png,It is clear that variable capital-gain is one of the three most relevant features.,1115
adult_decision_tree.png,It is clear that variable capital-loss is one of the three most relevant features.,1116
adult_decision_tree.png,It is clear that variable hours-per-week is one of the three most relevant features.,1117
adult_decision_tree.png,It is clear that variable age is one of the four most relevant features.,1118
adult_decision_tree.png,It is clear that variable fnlwgt is one of the four most relevant features.,1119
adult_decision_tree.png,It is clear that variable educational-num is one of the four most relevant features.,1120
adult_decision_tree.png,It is clear that variable capital-gain is one of the four most relevant features.,1121
adult_decision_tree.png,It is clear that variable capital-loss is one of the four most relevant features.,1122
adult_decision_tree.png,It is clear that variable hours-per-week is one of the four most relevant features.,1123
adult_decision_tree.png,It is clear that variable age is one of the five most relevant features.,1124
adult_decision_tree.png,It is clear that variable fnlwgt is one of the five most relevant features.,1125
adult_decision_tree.png,It is clear that variable educational-num is one of the five most relevant features.,1126
adult_decision_tree.png,It is clear that variable capital-gain is one of the five most relevant features.,1127
adult_decision_tree.png,It is clear that variable capital-loss is one of the five most relevant features.,1128
adult_decision_tree.png,It is clear that variable hours-per-week is one of the five most relevant features.,1129
adult_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",1130
adult_correlation_heatmap.png,Variables fnlwgt and age are redundant.,1131
adult_correlation_heatmap.png,Variables educational-num and age are redundant.,1132
adult_correlation_heatmap.png,Variables capital-gain and age are redundant.,1133
adult_correlation_heatmap.png,Variables capital-loss and age are redundant.,1134
adult_correlation_heatmap.png,Variables hours-per-week and age are redundant.,1135
adult_correlation_heatmap.png,Variables age and fnlwgt are redundant.,1136
adult_correlation_heatmap.png,Variables educational-num and fnlwgt are redundant.,1137
adult_correlation_heatmap.png,Variables capital-gain and fnlwgt are redundant.,1138
adult_correlation_heatmap.png,Variables capital-loss and fnlwgt are redundant.,1139
adult_correlation_heatmap.png,Variables hours-per-week and fnlwgt are redundant.,1140
adult_correlation_heatmap.png,Variables age and educational-num are redundant.,1141
adult_correlation_heatmap.png,Variables fnlwgt and educational-num are redundant.,1142
adult_correlation_heatmap.png,Variables capital-gain and educational-num are redundant.,1143
adult_correlation_heatmap.png,Variables capital-loss and educational-num are redundant.,1144
adult_correlation_heatmap.png,Variables hours-per-week and educational-num are redundant.,1145
adult_correlation_heatmap.png,Variables age and capital-gain are redundant.,1146
adult_correlation_heatmap.png,Variables fnlwgt and capital-gain are redundant.,1147
adult_correlation_heatmap.png,Variables educational-num and capital-gain are redundant.,1148
adult_correlation_heatmap.png,Variables capital-loss and capital-gain are redundant.,1149
adult_correlation_heatmap.png,Variables hours-per-week and capital-gain are redundant.,1150
adult_correlation_heatmap.png,Variables age and capital-loss are redundant.,1151
adult_correlation_heatmap.png,Variables fnlwgt and capital-loss are redundant.,1152
adult_correlation_heatmap.png,Variables educational-num and capital-loss are redundant.,1153
adult_correlation_heatmap.png,Variables capital-gain and capital-loss are redundant.,1154
adult_correlation_heatmap.png,Variables hours-per-week and capital-loss are redundant.,1155
adult_correlation_heatmap.png,Variables age and hours-per-week are redundant.,1156
adult_correlation_heatmap.png,Variables fnlwgt and hours-per-week are redundant.,1157
adult_correlation_heatmap.png,Variables educational-num and hours-per-week are redundant.,1158
adult_correlation_heatmap.png,Variables capital-gain and hours-per-week are redundant.,1159
adult_correlation_heatmap.png,Variables capital-loss and hours-per-week are redundant.,1160
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and educational-num.",1161
adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair workclass and capital-gain.",1162
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair workclass and hours-per-week.",1163
adult_correlation_heatmap.png,"Variables age and capital-loss are redundant, but we can’t say the same for the pair education and marital-status.",1164
adult_correlation_heatmap.png,"Variables relationship and capital-gain are redundant, but we can’t say the same for the pair fnlwgt and capital-loss.",1165
adult_correlation_heatmap.png,"Variables workclass and relationship are redundant, but we can’t say the same for the pair fnlwgt and education.",1166
adult_correlation_heatmap.png,"Variables age and capital-loss are redundant, but we can’t say the same for the pair workclass and marital-status.",1167
adult_correlation_heatmap.png,"Variables education and capital-gain are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1168
adult_correlation_heatmap.png,"Variables fnlwgt and capital-gain are redundant, but we can’t say the same for the pair workclass and relationship.",1169
adult_correlation_heatmap.png,"Variables workclass and occupation are redundant, but we can’t say the same for the pair age and relationship.",1170
adult_correlation_heatmap.png,"Variables age and fnlwgt are redundant, but we can’t say the same for the pair workclass and marital-status.",1171
adult_correlation_heatmap.png,"Variables education and gender are redundant, but we can’t say the same for the pair marital-status and relationship.",1172
adult_correlation_heatmap.png,"Variables educational-num and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and gender.",1173
adult_correlation_heatmap.png,"Variables race and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and capital-gain.",1174
adult_correlation_heatmap.png,"Variables education and educational-num are redundant, but we can’t say the same for the pair age and occupation.",1175
adult_correlation_heatmap.png,"Variables relationship and hours-per-week are redundant, but we can’t say the same for the pair education and occupation.",1176
adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair capital-gain and capital-loss.",1177
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair marital-status and hours-per-week.",1178
adult_correlation_heatmap.png,"Variables marital-status and occupation are redundant, but we can’t say the same for the pair fnlwgt and race.",1179
adult_correlation_heatmap.png,"Variables capital-loss and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and occupation.",1180
adult_correlation_heatmap.png,"Variables workclass and capital-gain are redundant, but we can’t say the same for the pair marital-status and gender.",1181
adult_correlation_heatmap.png,"Variables occupation and capital-gain are redundant, but we can’t say the same for the pair age and race.",1182
adult_correlation_heatmap.png,"Variables age and education are redundant, but we can’t say the same for the pair occupation and gender.",1183
adult_correlation_heatmap.png,"Variables age and hours-per-week are redundant, but we can’t say the same for the pair capital-gain and capital-loss.",1184
adult_correlation_heatmap.png,"Variables educational-num and gender are redundant, but we can’t say the same for the pair age and capital-loss.",1185
adult_correlation_heatmap.png,"Variables relationship and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and race.",1186
adult_correlation_heatmap.png,"Variables occupation and race are redundant, but we can’t say the same for the pair age and capital-loss.",1187
adult_correlation_heatmap.png,"Variables age and race are redundant, but we can’t say the same for the pair education and capital-gain.",1188
adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair gender and hours-per-week.",1189
adult_correlation_heatmap.png,"Variables workclass and occupation are redundant, but we can’t say the same for the pair capital-loss and hours-per-week.",1190
adult_correlation_heatmap.png,"Variables educational-num and hours-per-week are redundant, but we can’t say the same for the pair education and race.",1191
adult_correlation_heatmap.png,"Variables capital-loss and hours-per-week are redundant, but we can’t say the same for the pair workclass and fnlwgt.",1192
adult_correlation_heatmap.png,"Variables fnlwgt and capital-gain are redundant, but we can’t say the same for the pair age and relationship.",1193
adult_correlation_heatmap.png,"Variables age and occupation are redundant, but we can’t say the same for the pair workclass and relationship.",1194
adult_correlation_heatmap.png,"Variables educational-num and marital-status are redundant, but we can’t say the same for the pair gender and hours-per-week.",1195
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair gender and hours-per-week.",1196
adult_correlation_heatmap.png,"Variables educational-num and marital-status are redundant, but we can’t say the same for the pair fnlwgt and race.",1197
adult_correlation_heatmap.png,"Variables fnlwgt and race are redundant, but we can’t say the same for the pair relationship and hours-per-week.",1198
adult_correlation_heatmap.png,"Variables educational-num and relationship are redundant, but we can’t say the same for the pair education and marital-status.",1199
adult_correlation_heatmap.png,"Variables educational-num and race are redundant, but we can’t say the same for the pair workclass and capital-gain.",1200
adult_correlation_heatmap.png,"Variables workclass and gender are redundant, but we can’t say the same for the pair age and occupation.",1201
adult_correlation_heatmap.png,"Variables workclass and relationship are redundant, but we can’t say the same for the pair education and race.",1202
adult_correlation_heatmap.png,"Variables capital-gain and capital-loss are redundant, but we can’t say the same for the pair race and gender.",1203
adult_correlation_heatmap.png,"Variables relationship and capital-gain are redundant, but we can’t say the same for the pair age and fnlwgt.",1204
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair workclass and hours-per-week.",1205
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and race.",1206
adult_correlation_heatmap.png,"Variables educational-num and marital-status are redundant, but we can’t say the same for the pair workclass and education.",1207
adult_correlation_heatmap.png,"Variables age and relationship are redundant, but we can’t say the same for the pair education and educational-num.",1208
adult_correlation_heatmap.png,"Variables race and capital-loss are redundant, but we can’t say the same for the pair workclass and educational-num.",1209
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair workclass and relationship.",1210
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair education and gender.",1211
adult_correlation_heatmap.png,"Variables capital-gain and capital-loss are redundant, but we can’t say the same for the pair workclass and occupation.",1212
adult_correlation_heatmap.png,"Variables race and hours-per-week are redundant, but we can’t say the same for the pair marital-status and occupation.",1213
adult_correlation_heatmap.png,"Variables relationship and capital-gain are redundant, but we can’t say the same for the pair capital-loss and hours-per-week.",1214
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair workclass and capital-gain.",1215
adult_correlation_heatmap.png,"Variables age and occupation are redundant, but we can’t say the same for the pair workclass and marital-status.",1216
adult_correlation_heatmap.png,"Variables education and occupation are redundant, but we can’t say the same for the pair workclass and capital-gain.",1217
adult_correlation_heatmap.png,"Variables education and race are redundant, but we can’t say the same for the pair workclass and capital-gain.",1218
adult_correlation_heatmap.png,"Variables workclass and capital-loss are redundant, but we can’t say the same for the pair gender and capital-gain.",1219
adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair age and capital-gain.",1220
adult_correlation_heatmap.png,"Variables education and race are redundant, but we can’t say the same for the pair age and relationship.",1221
adult_correlation_heatmap.png,"Variables occupation and capital-loss are redundant, but we can’t say the same for the pair education and educational-num.",1222
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair age and education.",1223
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair workclass and fnlwgt.",1224
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair age and occupation.",1225
adult_correlation_heatmap.png,"Variables age and relationship are redundant, but we can’t say the same for the pair race and hours-per-week.",1226
adult_correlation_heatmap.png,"Variables fnlwgt and marital-status are redundant, but we can’t say the same for the pair education and relationship.",1227
adult_correlation_heatmap.png,"Variables race and hours-per-week are redundant, but we can’t say the same for the pair workclass and education.",1228
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair workclass and educational-num.",1229
adult_correlation_heatmap.png,"Variables relationship and hours-per-week are redundant, but we can’t say the same for the pair marital-status and race.",1230
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair race and capital-loss.",1231
adult_correlation_heatmap.png,"Variables age and capital-gain are redundant, but we can’t say the same for the pair workclass and relationship.",1232
adult_correlation_heatmap.png,"Variables race and capital-gain are redundant, but we can’t say the same for the pair age and gender.",1233
adult_correlation_heatmap.png,"Variables fnlwgt and occupation are redundant, but we can’t say the same for the pair marital-status and gender.",1234
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair relationship and gender.",1235
adult_correlation_heatmap.png,"Variables fnlwgt and education are redundant, but we can’t say the same for the pair educational-num and occupation.",1236
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair age and gender.",1237
adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair fnlwgt and educational-num.",1238
adult_correlation_heatmap.png,"Variables workclass and hours-per-week are redundant, but we can’t say the same for the pair marital-status and gender.",1239
adult_correlation_heatmap.png,"Variables race and capital-gain are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1240
adult_correlation_heatmap.png,"Variables education and capital-gain are redundant, but we can’t say the same for the pair age and race.",1241
adult_correlation_heatmap.png,"Variables fnlwgt and education are redundant, but we can’t say the same for the pair age and hours-per-week.",1242
adult_correlation_heatmap.png,"Variables educational-num and relationship are redundant, but we can’t say the same for the pair capital-loss and hours-per-week.",1243
adult_correlation_heatmap.png,"Variables capital-loss and hours-per-week are redundant, but we can’t say the same for the pair marital-status and occupation.",1244
adult_correlation_heatmap.png,"Variables educational-num and gender are redundant, but we can’t say the same for the pair capital-gain and capital-loss.",1245
adult_correlation_heatmap.png,"Variables marital-status and occupation are redundant, but we can’t say the same for the pair workclass and educational-num.",1246
adult_correlation_heatmap.png,"Variables age and occupation are redundant, but we can’t say the same for the pair workclass and race.",1247
adult_correlation_heatmap.png,"Variables education and gender are redundant, but we can’t say the same for the pair fnlwgt and relationship.",1248
adult_correlation_heatmap.png,"Variables educational-num and capital-gain are redundant, but we can’t say the same for the pair relationship and capital-loss.",1249
adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair educational-num and gender.",1250
adult_correlation_heatmap.png,"Variables workclass and education are redundant, but we can’t say the same for the pair relationship and capital-loss.",1251
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair occupation and hours-per-week.",1252
adult_correlation_heatmap.png,"Variables race and capital-gain are redundant, but we can’t say the same for the pair workclass and hours-per-week.",1253
adult_correlation_heatmap.png,"Variables age and race are redundant, but we can’t say the same for the pair education and capital-loss.",1254
adult_correlation_heatmap.png,"Variables occupation and capital-gain are redundant, but we can’t say the same for the pair workclass and education.",1255
adult_correlation_heatmap.png,"Variables age and capital-gain are redundant, but we can’t say the same for the pair education and marital-status.",1256
adult_correlation_heatmap.png,"Variables fnlwgt and education are redundant, but we can’t say the same for the pair race and gender.",1257
adult_correlation_heatmap.png,"Variables age and relationship are redundant, but we can’t say the same for the pair race and capital-loss.",1258
adult_correlation_heatmap.png,"Variables marital-status and capital-gain are redundant, but we can’t say the same for the pair fnlwgt and gender.",1259
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair education and capital-gain.",1260
adult_correlation_heatmap.png,"Variables education and occupation are redundant, but we can’t say the same for the pair relationship and hours-per-week.",1261
adult_correlation_heatmap.png,"Variables workclass and occupation are redundant, but we can’t say the same for the pair marital-status and relationship.",1262
adult_correlation_heatmap.png,"Variables age and workclass are redundant, but we can’t say the same for the pair marital-status and capital-gain.",1263
adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair race and capital-loss.",1264
adult_correlation_heatmap.png,"Variables age and hours-per-week are redundant, but we can’t say the same for the pair occupation and capital-loss.",1265
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair educational-num and race.",1266
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair relationship and capital-loss.",1267
adult_correlation_heatmap.png,"Variables relationship and gender are redundant, but we can’t say the same for the pair age and hours-per-week.",1268
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair age and educational-num.",1269
adult_correlation_heatmap.png,"Variables education and relationship are redundant, but we can’t say the same for the pair race and hours-per-week.",1270
adult_correlation_heatmap.png,"Variables educational-num and capital-gain are redundant, but we can’t say the same for the pair marital-status and relationship.",1271
adult_correlation_heatmap.png,"Variables relationship and gender are redundant, but we can’t say the same for the pair workclass and race.",1272
adult_correlation_heatmap.png,"Variables workclass and capital-gain are redundant, but we can’t say the same for the pair age and marital-status.",1273
adult_correlation_heatmap.png,"Variables marital-status and capital-loss are redundant, but we can’t say the same for the pair relationship and race.",1274
adult_correlation_heatmap.png,"Variables marital-status and gender are redundant, but we can’t say the same for the pair age and fnlwgt.",1275
adult_correlation_heatmap.png,"Variables age and race are redundant, but we can’t say the same for the pair relationship and capital-gain.",1276
adult_correlation_heatmap.png,"Variables education and marital-status are redundant, but we can’t say the same for the pair fnlwgt and gender.",1277
adult_correlation_heatmap.png,"Variables marital-status and relationship are redundant, but we can’t say the same for the pair occupation and capital-loss.",1278
adult_correlation_heatmap.png,"Variables occupation and capital-gain are redundant, but we can’t say the same for the pair relationship and gender.",1279
adult_correlation_heatmap.png,"Variables workclass and marital-status are redundant, but we can’t say the same for the pair age and capital-loss.",1280
adult_correlation_heatmap.png,"Variables marital-status and hours-per-week are redundant, but we can’t say the same for the pair workclass and educational-num.",1281
adult_correlation_heatmap.png,"Variables fnlwgt and capital-loss are redundant, but we can’t say the same for the pair age and workclass.",1282
adult_correlation_heatmap.png,"Variables education and capital-loss are redundant, but we can’t say the same for the pair relationship and race.",1283
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair age and fnlwgt.",1284
adult_correlation_heatmap.png,"Variables education and capital-gain are redundant, but we can’t say the same for the pair workclass and occupation.",1285
adult_correlation_heatmap.png,"Variables workclass and capital-loss are redundant, but we can’t say the same for the pair education and capital-gain.",1286
adult_correlation_heatmap.png,"Variables marital-status and capital-gain are redundant, but we can’t say the same for the pair race and hours-per-week.",1287
adult_correlation_heatmap.png,"Variables capital-loss and hours-per-week are redundant, but we can’t say the same for the pair age and relationship.",1288
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair age and capital-loss.",1289
adult_correlation_heatmap.png,"Variables workclass and occupation are redundant, but we can’t say the same for the pair relationship and capital-loss.",1290
adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair marital-status and race.",1291
adult_correlation_heatmap.png,"Variables age and education are redundant, but we can’t say the same for the pair workclass and capital-loss.",1292
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair educational-num and capital-gain.",1293
adult_correlation_heatmap.png,"Variables education and gender are redundant, but we can’t say the same for the pair fnlwgt and hours-per-week.",1294
adult_correlation_heatmap.png,"Variables fnlwgt and race are redundant, but we can’t say the same for the pair education and gender.",1295
adult_correlation_heatmap.png,"Variables marital-status and race are redundant, but we can’t say the same for the pair occupation and capital-loss.",1296
adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair workclass and capital-loss.",1297
adult_correlation_heatmap.png,"Variables educational-num and capital-gain are redundant, but we can’t say the same for the pair fnlwgt and gender.",1298
adult_correlation_heatmap.png,"Variables fnlwgt and gender are redundant, but we can’t say the same for the pair age and education.",1299
adult_correlation_heatmap.png,"Variables educational-num and capital-gain are redundant, but we can’t say the same for the pair workclass and hours-per-week.",1300
adult_correlation_heatmap.png,"Variables race and capital-gain are redundant, but we can’t say the same for the pair workclass and capital-loss.",1301
adult_correlation_heatmap.png,"Variables age and marital-status are redundant, but we can’t say the same for the pair education and capital-gain.",1302
adult_correlation_heatmap.png,"Variables educational-num and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and race.",1303
adult_correlation_heatmap.png,"Variables fnlwgt and gender are redundant, but we can’t say the same for the pair relationship and hours-per-week.",1304
adult_correlation_heatmap.png,"Variables educational-num and gender are redundant, but we can’t say the same for the pair age and education.",1305
adult_correlation_heatmap.png,"Variables age and hours-per-week are redundant, but we can’t say the same for the pair workclass and education.",1306
adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair fnlwgt and marital-status.",1307
adult_correlation_heatmap.png,"Variables marital-status and occupation are redundant, but we can’t say the same for the pair gender and capital-gain.",1308
adult_correlation_heatmap.png,"Variables marital-status and relationship are redundant, but we can’t say the same for the pair educational-num and capital-gain.",1309
adult_correlation_heatmap.png,"Variables race and capital-gain are redundant, but we can’t say the same for the pair age and capital-loss.",1310
adult_correlation_heatmap.png,"Variables relationship and gender are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1311
adult_correlation_heatmap.png,"Variables fnlwgt and occupation are redundant, but we can’t say the same for the pair age and workclass.",1312
adult_correlation_heatmap.png,"Variables relationship and hours-per-week are redundant, but we can’t say the same for the pair marital-status and gender.",1313
adult_correlation_heatmap.png,"Variables educational-num and hours-per-week are redundant, but we can’t say the same for the pair workclass and capital-gain.",1314
adult_correlation_heatmap.png,"Variables workclass and educational-num are redundant, but we can’t say the same for the pair age and capital-gain.",1315
adult_correlation_heatmap.png,"Variables race and gender are redundant, but we can’t say the same for the pair education and educational-num.",1316
adult_correlation_heatmap.png,"Variables capital-gain and capital-loss are redundant, but we can’t say the same for the pair workclass and relationship.",1317
adult_correlation_heatmap.png,"Variables education and educational-num are redundant, but we can’t say the same for the pair fnlwgt and marital-status.",1318
adult_correlation_heatmap.png,"Variables relationship and gender are redundant, but we can’t say the same for the pair educational-num and capital-gain.",1319
adult_correlation_heatmap.png,"Variables workclass and marital-status are redundant, but we can’t say the same for the pair relationship and hours-per-week.",1320
adult_correlation_heatmap.png,"Variables marital-status and capital-loss are redundant, but we can’t say the same for the pair education and relationship.",1321
adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair age and occupation.",1322
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair fnlwgt and hours-per-week.",1323
adult_correlation_heatmap.png,"Variables age and capital-gain are redundant, but we can’t say the same for the pair marital-status and occupation.",1324
adult_correlation_heatmap.png,"Variables age and fnlwgt are redundant, but we can’t say the same for the pair capital-gain and hours-per-week.",1325
adult_correlation_heatmap.png,"Variables fnlwgt and occupation are redundant, but we can’t say the same for the pair age and educational-num.",1326
adult_correlation_heatmap.png,"Variables occupation and gender are redundant, but we can’t say the same for the pair relationship and race.",1327
adult_correlation_heatmap.png,"Variables educational-num and relationship are redundant, but we can’t say the same for the pair fnlwgt and capital-gain.",1328
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair education and educational-num.",1329
adult_correlation_heatmap.png,"Variables fnlwgt and occupation are redundant, but we can’t say the same for the pair age and race.",1330
adult_correlation_heatmap.png,"Variables educational-num and relationship are redundant, but we can’t say the same for the pair age and gender.",1331
adult_correlation_heatmap.png,"Variables workclass and relationship are redundant, but we can’t say the same for the pair race and gender.",1332
adult_correlation_heatmap.png,"Variables workclass and educational-num are redundant, but we can’t say the same for the pair race and hours-per-week.",1333
adult_correlation_heatmap.png,"Variables fnlwgt and marital-status are redundant, but we can’t say the same for the pair relationship and capital-loss.",1334
adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and capital-gain.",1335
adult_correlation_heatmap.png,"Variables workclass and education are redundant, but we can’t say the same for the pair marital-status and race.",1336
adult_correlation_heatmap.png,"Variables marital-status and race are redundant, but we can’t say the same for the pair workclass and hours-per-week.",1337
adult_correlation_heatmap.png,"Variables age and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and relationship.",1338
adult_correlation_heatmap.png,"Variables fnlwgt and hours-per-week are redundant, but we can’t say the same for the pair age and marital-status.",1339
adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair educational-num and occupation.",1340
adult_correlation_heatmap.png,"Variables workclass and hours-per-week are redundant, but we can’t say the same for the pair age and education.",1341
adult_correlation_heatmap.png,"Variables gender and capital-loss are redundant, but we can’t say the same for the pair race and hours-per-week.",1342
adult_correlation_heatmap.png,"Variables age and occupation are redundant, but we can’t say the same for the pair educational-num and relationship.",1343
adult_correlation_heatmap.png,"Variables education and occupation are redundant, but we can’t say the same for the pair capital-gain and capital-loss.",1344
adult_correlation_heatmap.png,"Variables workclass and educational-num are redundant, but we can’t say the same for the pair relationship and gender.",1345
adult_correlation_heatmap.png,"Variables race and capital-loss are redundant, but we can’t say the same for the pair education and gender.",1346
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair capital-gain and hours-per-week.",1347
adult_correlation_heatmap.png,"Variables relationship and hours-per-week are redundant, but we can’t say the same for the pair educational-num and marital-status.",1348
adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair age and educational-num.",1349
adult_correlation_heatmap.png,"Variables occupation and gender are redundant, but we can’t say the same for the pair workclass and education.",1350
adult_correlation_heatmap.png,"Variables marital-status and capital-loss are redundant, but we can’t say the same for the pair education and capital-gain.",1351
adult_correlation_heatmap.png,"Variables age and race are redundant, but we can’t say the same for the pair education and occupation.",1352
adult_correlation_heatmap.png,"Variables educational-num and capital-gain are redundant, but we can’t say the same for the pair age and marital-status.",1353
adult_correlation_heatmap.png,"Variables workclass and capital-loss are redundant, but we can’t say the same for the pair age and relationship.",1354
adult_correlation_heatmap.png,"Variables educational-num and capital-gain are redundant, but we can’t say the same for the pair occupation and relationship.",1355
adult_correlation_heatmap.png,"Variables age and occupation are redundant, but we can’t say the same for the pair race and hours-per-week.",1356
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair fnlwgt and education.",1357
adult_correlation_heatmap.png,"Variables education and relationship are redundant, but we can’t say the same for the pair marital-status and gender.",1358
adult_correlation_heatmap.png,"Variables educational-num and hours-per-week are redundant, but we can’t say the same for the pair occupation and relationship.",1359
adult_correlation_heatmap.png,"Variables age and hours-per-week are redundant, but we can’t say the same for the pair workclass and gender.",1360
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair workclass and race.",1361
adult_correlation_heatmap.png,"Variables age and capital-gain are redundant, but we can’t say the same for the pair educational-num and relationship.",1362
adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair age and workclass.",1363
adult_correlation_heatmap.png,"Variables marital-status and race are redundant, but we can’t say the same for the pair education and relationship.",1364
adult_correlation_heatmap.png,"Variables marital-status and race are redundant, but we can’t say the same for the pair fnlwgt and capital-gain.",1365
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair educational-num and relationship.",1366
adult_correlation_heatmap.png,"Variables gender and capital-gain are redundant, but we can’t say the same for the pair age and relationship.",1367
adult_correlation_heatmap.png,"Variables workclass and gender are redundant, but we can’t say the same for the pair educational-num and capital-loss.",1368
adult_correlation_heatmap.png,"Variables education and educational-num are redundant, but we can’t say the same for the pair capital-gain and hours-per-week.",1369
adult_correlation_heatmap.png,"Variables educational-num and capital-loss are redundant, but we can’t say the same for the pair age and workclass.",1370
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair capital-gain and capital-loss.",1371
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair workclass and fnlwgt.",1372
adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair workclass and marital-status.",1373
adult_correlation_heatmap.png,"Variables fnlwgt and race are redundant, but we can’t say the same for the pair gender and hours-per-week.",1374
adult_correlation_heatmap.png,"Variables race and gender are redundant, but we can’t say the same for the pair relationship and hours-per-week.",1375
adult_correlation_heatmap.png,"Variables occupation and hours-per-week are redundant, but we can’t say the same for the pair educational-num and gender.",1376
adult_correlation_heatmap.png,"Variables workclass and capital-gain are redundant, but we can’t say the same for the pair age and gender.",1377
adult_correlation_heatmap.png,"Variables age and marital-status are redundant, but we can’t say the same for the pair educational-num and capital-gain.",1378
adult_correlation_heatmap.png,"Variables education and gender are redundant, but we can’t say the same for the pair educational-num and relationship.",1379
adult_correlation_heatmap.png,"Variables workclass and occupation are redundant, but we can’t say the same for the pair fnlwgt and relationship.",1380
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1381
adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair education and marital-status.",1382
adult_correlation_heatmap.png,"Variables fnlwgt and marital-status are redundant, but we can’t say the same for the pair gender and capital-loss.",1383
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair educational-num and capital-loss.",1384
adult_correlation_heatmap.png,"Variables age and capital-gain are redundant, but we can’t say the same for the pair fnlwgt and education.",1385
adult_correlation_heatmap.png,"Variables capital-loss and hours-per-week are redundant, but we can’t say the same for the pair age and workclass.",1386
adult_correlation_heatmap.png,"Variables education and gender are redundant, but we can’t say the same for the pair occupation and capital-gain.",1387
adult_correlation_heatmap.png,"Variables age and race are redundant, but we can’t say the same for the pair workclass and educational-num.",1388
adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair gender and capital-gain.",1389
adult_correlation_heatmap.png,"Variables educational-num and capital-gain are redundant, but we can’t say the same for the pair education and relationship.",1390
adult_correlation_heatmap.png,"Variables fnlwgt and capital-gain are redundant, but we can’t say the same for the pair education and marital-status.",1391
adult_correlation_heatmap.png,"Variables age and fnlwgt are redundant, but we can’t say the same for the pair gender and capital-gain.",1392
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair occupation and gender.",1393
adult_correlation_heatmap.png,"Variables race and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and occupation.",1394
adult_correlation_heatmap.png,"Variables workclass and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and gender.",1395
adult_correlation_heatmap.png,"Variables occupation and hours-per-week are redundant, but we can’t say the same for the pair education and gender.",1396
adult_correlation_heatmap.png,"Variables race and capital-loss are redundant, but we can’t say the same for the pair marital-status and gender.",1397
adult_correlation_heatmap.png,"Variables race and gender are redundant, but we can’t say the same for the pair fnlwgt and marital-status.",1398
adult_correlation_heatmap.png,"Variables capital-gain and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and occupation.",1399
adult_correlation_heatmap.png,"Variables capital-gain and capital-loss are redundant, but we can’t say the same for the pair educational-num and occupation.",1400
adult_correlation_heatmap.png,"Variables fnlwgt and occupation are redundant, but we can’t say the same for the pair relationship and race.",1401
adult_correlation_heatmap.png,"Variables fnlwgt and marital-status are redundant, but we can’t say the same for the pair occupation and capital-loss.",1402
adult_correlation_heatmap.png,"Variables occupation and relationship are redundant, but we can’t say the same for the pair educational-num and gender.",1403
adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair workclass and fnlwgt.",1404
adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1405
adult_correlation_heatmap.png,"Variables workclass and marital-status are redundant, but we can’t say the same for the pair educational-num and capital-loss.",1406
adult_correlation_heatmap.png,"Variables workclass and capital-loss are redundant, but we can’t say the same for the pair education and race.",1407
adult_correlation_heatmap.png,"Variables workclass and occupation are redundant, but we can’t say the same for the pair fnlwgt and education.",1408
adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair age and marital-status.",1409
adult_correlation_heatmap.png,"Variables workclass and hours-per-week are redundant, but we can’t say the same for the pair race and gender.",1410
adult_correlation_heatmap.png,"Variables race and hours-per-week are redundant, but we can’t say the same for the pair age and workclass.",1411
adult_correlation_heatmap.png,"Variables occupation and capital-gain are redundant, but we can’t say the same for the pair education and hours-per-week.",1412
adult_correlation_heatmap.png,"Variables relationship and hours-per-week are redundant, but we can’t say the same for the pair workclass and fnlwgt.",1413
adult_correlation_heatmap.png,"Variables occupation and gender are redundant, but we can’t say the same for the pair education and capital-loss.",1414
adult_correlation_heatmap.png,"Variables gender and capital-gain are redundant, but we can’t say the same for the pair fnlwgt and marital-status.",1415
adult_correlation_heatmap.png,"Variables marital-status and hours-per-week are redundant, but we can’t say the same for the pair fnlwgt and education.",1416
adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair race and hours-per-week.",1417
adult_correlation_heatmap.png,"Variables age and relationship are redundant, but we can’t say the same for the pair workclass and education.",1418
adult_correlation_heatmap.png,"Variables fnlwgt and marital-status are redundant, but we can’t say the same for the pair age and gender.",1419
adult_correlation_heatmap.png,"Variables education and relationship are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1420
adult_correlation_heatmap.png,"Variables fnlwgt and capital-loss are redundant, but we can’t say the same for the pair marital-status and occupation.",1421
adult_correlation_heatmap.png,"Variables fnlwgt and relationship are redundant, but we can’t say the same for the pair capital-gain and hours-per-week.",1422
adult_correlation_heatmap.png,"Variables relationship and hours-per-week are redundant, but we can’t say the same for the pair workclass and education.",1423
adult_correlation_heatmap.png,"Variables marital-status and capital-loss are redundant, but we can’t say the same for the pair age and occupation.",1424
adult_correlation_heatmap.png,"Variables workclass and gender are redundant, but we can’t say the same for the pair educational-num and capital-gain.",1425
adult_correlation_heatmap.png,"Variables gender and capital-loss are redundant, but we can’t say the same for the pair educational-num and race.",1426
adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair marital-status and race.",1427
adult_correlation_heatmap.png,"Variables education and relationship are redundant, but we can’t say the same for the pair marital-status and occupation.",1428
adult_correlation_heatmap.png,"Variables fnlwgt and marital-status are redundant, but we can’t say the same for the pair race and capital-loss.",1429
adult_correlation_heatmap.png,"Variables age and education are redundant, but we can’t say the same for the pair relationship and hours-per-week.",1430
adult_correlation_heatmap.png,"Variables workclass and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and occupation.",1431
adult_correlation_heatmap.png,"Variables race and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and hours-per-week.",1432
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair marital-status and hours-per-week.",1433
adult_correlation_heatmap.png,"Variables race and gender are redundant, but we can’t say the same for the pair education and capital-gain.",1434
adult_correlation_heatmap.png,"Variables age and educational-num are redundant, but we can’t say the same for the pair occupation and gender.",1435
adult_correlation_heatmap.png,"Variables age and relationship are redundant, but we can’t say the same for the pair educational-num and capital-gain.",1436
adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair age and educational-num.",1437
adult_correlation_heatmap.png,"Variables marital-status and capital-loss are redundant, but we can’t say the same for the pair gender and capital-gain.",1438
adult_correlation_heatmap.png,"Variables educational-num and hours-per-week are redundant, but we can’t say the same for the pair education and marital-status.",1439
adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair education and relationship.",1440
adult_correlation_heatmap.png,"Variables age and occupation are redundant, but we can’t say the same for the pair workclass and capital-loss.",1441
adult_correlation_heatmap.png,"Variables workclass and gender are redundant, but we can’t say the same for the pair age and race.",1442
adult_correlation_heatmap.png,"Variables age and marital-status are redundant, but we can’t say the same for the pair gender and capital-loss.",1443
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair occupation and race.",1444
adult_correlation_heatmap.png,"Variables educational-num and relationship are redundant, but we can’t say the same for the pair education and hours-per-week.",1445
adult_correlation_heatmap.png,"Variables educational-num and capital-loss are redundant, but we can’t say the same for the pair education and gender.",1446
adult_correlation_heatmap.png,"Variables age and marital-status are redundant, but we can’t say the same for the pair relationship and capital-gain.",1447
adult_correlation_heatmap.png,"Variables fnlwgt and capital-gain are redundant, but we can’t say the same for the pair age and hours-per-week.",1448
adult_correlation_heatmap.png,"Variables workclass and gender are redundant, but we can’t say the same for the pair age and educational-num.",1449
adult_correlation_heatmap.png,"Variables education and marital-status are redundant, but we can’t say the same for the pair educational-num and race.",1450
adult_correlation_heatmap.png,"Variables workclass and capital-gain are redundant, but we can’t say the same for the pair occupation and race.",1451
adult_correlation_heatmap.png,"Variables gender and capital-loss are redundant, but we can’t say the same for the pair relationship and hours-per-week.",1452
adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair marital-status and gender.",1453
adult_correlation_heatmap.png,"Variables capital-gain and capital-loss are redundant, but we can’t say the same for the pair marital-status and gender.",1454
adult_correlation_heatmap.png,"Variables marital-status and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and relationship.",1455
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and occupation.",1456
adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair relationship and race.",1457
adult_correlation_heatmap.png,"Variables workclass and relationship are redundant, but we can’t say the same for the pair race and capital-loss.",1458
adult_correlation_heatmap.png,"Variables educational-num and race are redundant, but we can’t say the same for the pair fnlwgt and relationship.",1459
adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair educational-num and hours-per-week.",1460
adult_correlation_heatmap.png,"Variables education and marital-status are redundant, but we can’t say the same for the pair gender and capital-loss.",1461
adult_correlation_heatmap.png,"Variables age and capital-gain are redundant, but we can’t say the same for the pair workclass and capital-loss.",1462
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair education and hours-per-week.",1463
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair workclass and fnlwgt.",1464
adult_correlation_heatmap.png,"Variables workclass and race are redundant, but we can’t say the same for the pair occupation and capital-gain.",1465
adult_correlation_heatmap.png,"Variables marital-status and relationship are redundant, but we can’t say the same for the pair educational-num and gender.",1466
adult_correlation_heatmap.png,"Variables fnlwgt and education are redundant, but we can’t say the same for the pair relationship and race.",1467
adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair age and fnlwgt.",1468
adult_correlation_heatmap.png,"Variables fnlwgt and capital-gain are redundant, but we can’t say the same for the pair relationship and gender.",1469
adult_correlation_heatmap.png,"Variables gender and capital-gain are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1470
adult_correlation_heatmap.png,"Variables marital-status and gender are redundant, but we can’t say the same for the pair age and occupation.",1471
adult_correlation_heatmap.png,"Variables educational-num and race are redundant, but we can’t say the same for the pair capital-gain and hours-per-week.",1472
adult_correlation_heatmap.png,The variable age can be discarded without risking losing information.,1473
adult_correlation_heatmap.png,The variable fnlwgt can be discarded without risking losing information.,1474
adult_correlation_heatmap.png,The variable educational-num can be discarded without risking losing information.,1475
adult_correlation_heatmap.png,The variable capital-gain can be discarded without risking losing information.,1476
adult_correlation_heatmap.png,The variable capital-loss can be discarded without risking losing information.,1477
adult_correlation_heatmap.png,The variable hours-per-week can be discarded without risking losing information.,1478
adult_correlation_heatmap.png,One of the variables fnlwgt or age can be discarded without losing information.,1479
adult_correlation_heatmap.png,One of the variables educational-num or age can be discarded without losing information.,1480
adult_correlation_heatmap.png,One of the variables capital-gain or age can be discarded without losing information.,1481
adult_correlation_heatmap.png,One of the variables capital-loss or age can be discarded without losing information.,1482
adult_correlation_heatmap.png,One of the variables hours-per-week or age can be discarded without losing information.,1483
adult_correlation_heatmap.png,One of the variables age or fnlwgt can be discarded without losing information.,1484
adult_correlation_heatmap.png,One of the variables educational-num or fnlwgt can be discarded without losing information.,1485
adult_correlation_heatmap.png,One of the variables capital-gain or fnlwgt can be discarded without losing information.,1486
adult_correlation_heatmap.png,One of the variables capital-loss or fnlwgt can be discarded without losing information.,1487
adult_correlation_heatmap.png,One of the variables hours-per-week or fnlwgt can be discarded without losing information.,1488
adult_correlation_heatmap.png,One of the variables age or educational-num can be discarded without losing information.,1489
adult_correlation_heatmap.png,One of the variables fnlwgt or educational-num can be discarded without losing information.,1490
adult_correlation_heatmap.png,One of the variables capital-gain or educational-num can be discarded without losing information.,1491
adult_correlation_heatmap.png,One of the variables capital-loss or educational-num can be discarded without losing information.,1492
adult_correlation_heatmap.png,One of the variables hours-per-week or educational-num can be discarded without losing information.,1493
adult_correlation_heatmap.png,One of the variables age or capital-gain can be discarded without losing information.,1494
adult_correlation_heatmap.png,One of the variables fnlwgt or capital-gain can be discarded without losing information.,1495
adult_correlation_heatmap.png,One of the variables educational-num or capital-gain can be discarded without losing information.,1496
adult_correlation_heatmap.png,One of the variables capital-loss or capital-gain can be discarded without losing information.,1497
adult_correlation_heatmap.png,One of the variables hours-per-week or capital-gain can be discarded without losing information.,1498
adult_correlation_heatmap.png,One of the variables age or capital-loss can be discarded without losing information.,1499
adult_correlation_heatmap.png,One of the variables fnlwgt or capital-loss can be discarded without losing information.,1500
adult_correlation_heatmap.png,One of the variables educational-num or capital-loss can be discarded without losing information.,1501
adult_correlation_heatmap.png,One of the variables capital-gain or capital-loss can be discarded without losing information.,1502
adult_correlation_heatmap.png,One of the variables hours-per-week or capital-loss can be discarded without losing information.,1503
adult_correlation_heatmap.png,One of the variables age or hours-per-week can be discarded without losing information.,1504
adult_correlation_heatmap.png,One of the variables fnlwgt or hours-per-week can be discarded without losing information.,1505
adult_correlation_heatmap.png,One of the variables educational-num or hours-per-week can be discarded without losing information.,1506
adult_correlation_heatmap.png,One of the variables capital-gain or hours-per-week can be discarded without losing information.,1507
adult_correlation_heatmap.png,One of the variables capital-loss or hours-per-week can be discarded without losing information.,1508
adult_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,1509
adult_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,1510
adult_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,1511
adult_histograms_numeric.png,At least 50 of the variables present outliers.,1512
adult_boxplots.png,At least 60 of the variables present outliers.,1513
adult_boxplots.png,At least 75 of the variables present outliers.,1514
adult_histograms_numeric.png,At least 85 of the variables present outliers.,1515
adult_histograms_numeric.png,Variable age presents some outliers.,1516
adult_boxplots.png,Variable fnlwgt presents some outliers.,1517
adult_boxplots.png,Variable educational-num presents some outliers.,1518
adult_boxplots.png,Variable capital-gain presents some outliers.,1519
adult_histograms_numeric.png,Variable capital-loss presents some outliers.,1520
adult_boxplots.png,Variable hours-per-week presents some outliers.,1521
adult_histograms_numeric.png,Variable age doesn’t have any outliers.,1522
adult_histograms_numeric.png,Variable fnlwgt doesn’t have any outliers.,1523
adult_histograms_numeric.png,Variable educational-num doesn’t have any outliers.,1524
adult_boxplots.png,Variable capital-gain doesn’t have any outliers.,1525
adult_boxplots.png,Variable capital-loss doesn’t have any outliers.,1526
adult_boxplots.png,Variable hours-per-week doesn’t have any outliers.,1527
adult_boxplots.png,Variable age shows some outlier values.,1528
adult_histograms_numeric.png,Variable fnlwgt shows some outlier values.,1529
adult_histograms_numeric.png,Variable educational-num shows some outlier values.,1530
adult_boxplots.png,Variable capital-gain shows some outlier values.,1531
adult_histograms_numeric.png,Variable capital-loss shows some outlier values.,1532
adult_boxplots.png,Variable hours-per-week shows some outlier values.,1533
adult_histograms_numeric.png,Variable age shows a high number of outlier values.,1534
adult_boxplots.png,Variable fnlwgt shows a high number of outlier values.,1535
adult_boxplots.png,Variable educational-num shows a high number of outlier values.,1536
adult_boxplots.png,Variable capital-gain shows a high number of outlier values.,1537
adult_boxplots.png,Variable capital-loss shows a high number of outlier values.,1538
adult_histograms_numeric.png,Variable hours-per-week shows a high number of outlier values.,1539
adult_histograms_numeric.png,Outliers seem to be a problem in the dataset.,1540
adult_boxplots.png,"It is clear that variable fnlwgt shows some outliers, but we can’t be sure of the same for variable age.",1541
adult_histograms_numeric.png,"It is clear that variable educational-num shows some outliers, but we can’t be sure of the same for variable age.",1542
adult_histograms_numeric.png,"It is clear that variable capital-gain shows some outliers, but we can’t be sure of the same for variable age.",1543
adult_boxplots.png,"It is clear that variable capital-loss shows some outliers, but we can’t be sure of the same for variable age.",1544
adult_histograms_numeric.png,"It is clear that variable hours-per-week shows some outliers, but we can’t be sure of the same for variable age.",1545
adult_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable fnlwgt.",1546
adult_boxplots.png,"It is clear that variable educational-num shows some outliers, but we can’t be sure of the same for variable fnlwgt.",1547
adult_histograms_numeric.png,"It is clear that variable capital-gain shows some outliers, but we can’t be sure of the same for variable fnlwgt.",1548
adult_histograms_numeric.png,"It is clear that variable capital-loss shows some outliers, but we can’t be sure of the same for variable fnlwgt.",1549
adult_histograms_numeric.png,"It is clear that variable hours-per-week shows some outliers, but we can’t be sure of the same for variable fnlwgt.",1550
adult_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable educational-num.",1551
adult_boxplots.png,"It is clear that variable fnlwgt shows some outliers, but we can’t be sure of the same for variable educational-num.",1552
adult_histograms_numeric.png,"It is clear that variable capital-gain shows some outliers, but we can’t be sure of the same for variable educational-num.",1553
adult_boxplots.png,"It is clear that variable capital-loss shows some outliers, but we can’t be sure of the same for variable educational-num.",1554
adult_histograms_numeric.png,"It is clear that variable hours-per-week shows some outliers, but we can’t be sure of the same for variable educational-num.",1555
adult_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable capital-gain.",1556
adult_histograms_numeric.png,"It is clear that variable fnlwgt shows some outliers, but we can’t be sure of the same for variable capital-gain.",1557
adult_boxplots.png,"It is clear that variable educational-num shows some outliers, but we can’t be sure of the same for variable capital-gain.",1558
adult_boxplots.png,"It is clear that variable capital-loss shows some outliers, but we can’t be sure of the same for variable capital-gain.",1559
adult_boxplots.png,"It is clear that variable hours-per-week shows some outliers, but we can’t be sure of the same for variable capital-gain.",1560
adult_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable capital-loss.",1561
adult_histograms_numeric.png,"It is clear that variable fnlwgt shows some outliers, but we can’t be sure of the same for variable capital-loss.",1562
adult_histograms_numeric.png,"It is clear that variable educational-num shows some outliers, but we can’t be sure of the same for variable capital-loss.",1563
adult_boxplots.png,"It is clear that variable capital-gain shows some outliers, but we can’t be sure of the same for variable capital-loss.",1564
adult_histograms_numeric.png,"It is clear that variable hours-per-week shows some outliers, but we can’t be sure of the same for variable capital-loss.",1565
adult_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable hours-per-week.",1566
adult_histograms_numeric.png,"It is clear that variable fnlwgt shows some outliers, but we can’t be sure of the same for variable hours-per-week.",1567
adult_boxplots.png,"It is clear that variable educational-num shows some outliers, but we can’t be sure of the same for variable hours-per-week.",1568
adult_boxplots.png,"It is clear that variable capital-gain shows some outliers, but we can’t be sure of the same for variable hours-per-week.",1569
adult_histograms_numeric.png,"It is clear that variable capital-loss shows some outliers, but we can’t be sure of the same for variable hours-per-week.",1570
adult_boxplots.png,Those boxplots show that the data is not normalized.,1571
adult_boxplots.png,Variable age is balanced.,1572
adult_histograms_numeric.png,Variable fnlwgt is balanced.,1573
adult_histograms_numeric.png,Variable educational-num is balanced.,1574
adult_histograms_numeric.png,Variable capital-gain is balanced.,1575
adult_histograms_numeric.png,Variable capital-loss is balanced.,1576
adult_histograms_numeric.png,Variable hours-per-week is balanced.,1577
adult_histograms.png,The variable age can be seen as ordinal without losing information.,1578
adult_histograms.png,The variable workclass can be seen as ordinal without losing information.,1579
adult_histograms.png,The variable fnlwgt can be seen as ordinal without losing information.,1580
adult_histograms.png,The variable education can be seen as ordinal without losing information.,1581
adult_histograms.png,The variable educational-num can be seen as ordinal without losing information.,1582
adult_histograms.png,The variable marital-status can be seen as ordinal without losing information.,1583
adult_histograms.png,The variable occupation can be seen as ordinal without losing information.,1584
adult_histograms.png,The variable relationship can be seen as ordinal without losing information.,1585
adult_histograms.png,The variable race can be seen as ordinal without losing information.,1586
adult_histograms.png,The variable gender can be seen as ordinal without losing information.,1587
adult_histograms.png,The variable capital-gain can be seen as ordinal without losing information.,1588
adult_histograms.png,The variable capital-loss can be seen as ordinal without losing information.,1589
adult_histograms.png,The variable hours-per-week can be seen as ordinal without losing information.,1590
adult_histograms.png,The variable age can be seen as ordinal.,1591
adult_histograms.png,The variable workclass can be seen as ordinal.,1592
adult_histograms.png,The variable fnlwgt can be seen as ordinal.,1593
adult_histograms.png,The variable education can be seen as ordinal.,1594
adult_histograms.png,The variable educational-num can be seen as ordinal.,1595
adult_histograms.png,The variable marital-status can be seen as ordinal.,1596
adult_histograms.png,The variable occupation can be seen as ordinal.,1597
adult_histograms.png,The variable relationship can be seen as ordinal.,1598
adult_histograms.png,The variable race can be seen as ordinal.,1599
adult_histograms.png,The variable gender can be seen as ordinal.,1600
adult_histograms.png,The variable capital-gain can be seen as ordinal.,1601
adult_histograms.png,The variable capital-loss can be seen as ordinal.,1602
adult_histograms.png,The variable hours-per-week can be seen as ordinal.,1603
adult_histograms.png,"All variables, but the class, should be dealt with as numeric.",1604
adult_histograms.png,"All variables, but the class, should be dealt with as binary.",1605
adult_histograms.png,"All variables, but the class, should be dealt with as date.",1606
adult_histograms.png,"All variables, but the class, should be dealt with as symbolic.",1607
adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 89.,1608
adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 90.,1609
adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 54.,1610
adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 16.,1611
adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 32.,1612
adult_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,1613
adult_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",1614
adult_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",1615
adult_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",1616
adult_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",1617
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",1618
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",1619
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",1620
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",1621
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 0.",1622
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 0.",1623
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 0.",1624
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 0.",1625
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 4831.",1626
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 4831.",1627
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 4831.",1628
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 4831.",1629
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 4831.",1630
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 4831.",1631
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 4831.",1632
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 4831.",1633
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 114.",1634
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 114.",1635
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 114.",1636
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 114.",1637
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 114.",1638
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 114.",1639
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 114.",1640
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 114.",1641
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 1931.",1642
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 1931.",1643
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 1931.",1644
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 1931.",1645
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 1931.",1646
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 1931.",1647
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 1931.",1648
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 1931.",1649
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 124.",1650
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 124.",1651
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 124.",1652
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 124.",1653
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 124.",1654
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 124.",1655
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 124.",1656
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 124.",1657
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (A,B) as 1.",1658
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (not A, B) as 1.",1659
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (A, not B) as 1.",1660
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (not A, not B) as 1.",1661
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (A,B) as 0.",1662
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (not A, B) as 0.",1663
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (A, not B) as 0.",1664
Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (not A, not B) as 0.",1665
Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1293 episodes.,1666
Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1188 episodes.,1667
Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1274 episodes.,1668
Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1420 episodes.,1669
Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 627 episodes.,1670
Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 122 estimators.,1671
Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 96 estimators.,1672
Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 102 estimators.,1673
Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 107 estimators.,1674
Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 82 estimators.,1675
Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 76 estimators.,1676
Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 143 estimators.,1677
Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 87 estimators.,1678
Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 102 estimators.,1679
Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 150 estimators.,1680
Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,1681
Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,1682
Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,1683
Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,1684
Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,1685
Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,1686
Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,1687
Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,1688
Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,1689
Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,1690
Churn_Modelling_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,1691
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than its accuracy.,1692
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than its accuracy.,1693
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,1694
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than its accuracy.,1695
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than its accuracy.,1696
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,1697
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than its recall.,1698
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than its recall.,1699
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than its recall.,1700
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than its recall.,1701
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than its recall.,1702
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than its recall.,1703
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than its precision.,1704
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than its precision.,1705
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than its precision.,1706
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than its precision.,1707
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than its precision.,1708
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than its precision.,1709
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,1710
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than its specificity.,1711
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than its specificity.,1712
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,1713
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than its specificity.,1714
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than its specificity.,1715
Churn_Modelling_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,1716
Churn_Modelling_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,1717
Churn_Modelling_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,1718
Churn_Modelling_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,1719
Churn_Modelling_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,1720
Churn_Modelling_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,1721
Churn_Modelling_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,1722
Churn_Modelling_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,1723
Churn_Modelling_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,1724
Churn_Modelling_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,1725
Churn_Modelling_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,1726
Churn_Modelling_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,1727
Churn_Modelling_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,1728
Churn_Modelling_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,1729
Churn_Modelling_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,1730
Churn_Modelling_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,1731
Churn_Modelling_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,1732
Churn_Modelling_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,1733
Churn_Modelling_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,1734
Churn_Modelling_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,1735
Churn_Modelling_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,1736
Churn_Modelling_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,1737
Churn_Modelling_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,1738
Churn_Modelling_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,1739
Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 29.,1740
Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 26.,1741
Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 19.,1742
Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 49.,1743
Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 27.,1744
Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 41.,1745
Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 32.,1746
Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 33.,1747
Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 28.,1748
Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 13.,1749
Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 11.,1750
Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 35.,1751
Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 15.,1752
Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 48.,1753
Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 17.,1754
Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 14.,1755
Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 39.,1756
Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 10.,1757
Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 43.,1758
Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 40.,1759
Churn_Modelling_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,1760
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,1761
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,1762
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,1763
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,1764
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,1765
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,1766
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,1767
Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,1768
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,1769
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,1770
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,1771
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,1772
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,1773
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,1774
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,1775
Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,1776
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 88%.,1777
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 63%.,1778
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than 65%.,1779
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 66%.,1780
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 60%.,1781
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 82%.,1782
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 78%.,1783
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 89%.,1784
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 80%.,1785
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 77%.,1786
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than 90%.,1787
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 68%.,1788
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 62%.,1789
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 76%.,1790
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 85%.,1791
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 70%.,1792
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 81%.,1793
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 69%.,1794
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than 74%.,1795
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 75%.,1796
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 64%.,1797
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 86%.,1798
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 84%.,1799
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 61%.,1800
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 72%.,1801
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 87%.,1802
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than 71%.,1803
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 67%.,1804
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 83%.,1805
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 73%.,1806
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 79%.,1807
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 85%.,1808
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 60%.,1809
Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 89%.,1810
Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than 82%.,1811
Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 83%.,1812
Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 62%.,1813
Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 65%.,1814
Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 88%.,1815
Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 61%.,1816
Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",1817
Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",1818
Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",1819
Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",1820
Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",1821
Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",1822
Churn_Modelling_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,1823
Churn_Modelling_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,1824
Churn_Modelling_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,1825
Churn_Modelling_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,1826
Churn_Modelling_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,1827
Churn_Modelling_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,1828
Churn_Modelling_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,1829
Churn_Modelling_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,1830
Churn_Modelling_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,1831
Churn_Modelling_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,1832
Churn_Modelling_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,1833
Churn_Modelling_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,1834
Churn_Modelling_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,1835
Churn_Modelling_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,1836
Churn_Modelling_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,1837
Churn_Modelling_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,1838
Churn_Modelling_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,1839
Churn_Modelling_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,1840
Churn_Modelling_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,1841
Churn_Modelling_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,1842
Churn_Modelling_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,1843
Churn_Modelling_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,1844
Churn_Modelling_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,1845
Churn_Modelling_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,1846
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 2.,1847
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 2.,1848
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 3.,1849
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 3.,1850
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 4.,1851
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 4.,1852
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 5.,1853
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 5.,1854
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 6.,1855
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 6.,1856
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 7.,1857
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 7.,1858
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 8.,1859
Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 8.,1860
Churn_Modelling_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",1861
Churn_Modelling_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",1862
Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",1863
Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",1864
Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",1865
Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",1866
Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",1867
Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",1868
Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",1869
Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",1870
Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",1871
Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",1872
Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",1873
Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",1874
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,1875
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,1876
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,1877
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,1878
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,1879
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,1880
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,1881
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,1882
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,1883
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,1884
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,1885
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,1886
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,1887
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,1888
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,1889
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,1890
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,1891
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,1892
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,1893
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,1894
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,1895
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,1896
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,1897
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,1898
Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,1899
Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,1900
Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,1901
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable CreditScore.,1902
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Tenure previously than variable CreditScore.,1903
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable CreditScore.,1904
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable NumOfProducts previously than variable CreditScore.,1905
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable CreditScore.,1906
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CreditScore previously than variable Age.,1907
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Tenure previously than variable Age.,1908
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable Age.,1909
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable NumOfProducts previously than variable Age.,1910
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable Age.,1911
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CreditScore previously than variable Tenure.,1912
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Tenure.,1913
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable Tenure.,1914
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable NumOfProducts previously than variable Tenure.,1915
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable Tenure.,1916
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CreditScore previously than variable Balance.,1917
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Balance.,1918
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Tenure previously than variable Balance.,1919
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable NumOfProducts previously than variable Balance.,1920
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable Balance.,1921
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CreditScore previously than variable NumOfProducts.,1922
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable NumOfProducts.,1923
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Tenure previously than variable NumOfProducts.,1924
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable NumOfProducts.,1925
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable NumOfProducts.,1926
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CreditScore previously than variable EstimatedSalary.,1927
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable EstimatedSalary.,1928
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Tenure previously than variable EstimatedSalary.,1929
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable EstimatedSalary.,1930
Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable NumOfProducts previously than variable EstimatedSalary.,1931
Churn_Modelling_pca.png,The first 2 principal components are enough for explaining half the data variance.,1932
Churn_Modelling_pca.png,The first 3 principal components are enough for explaining half the data variance.,1933
Churn_Modelling_pca.png,The first 4 principal components are enough for explaining half the data variance.,1934
Churn_Modelling_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,1935
Churn_Modelling_correlation_heatmap.png,Removing variable CreditScore might improve the training of decision trees .,1936
Churn_Modelling_correlation_heatmap.png,Removing variable Age might improve the training of decision trees .,1937
Churn_Modelling_correlation_heatmap.png,Removing variable Tenure might improve the training of decision trees .,1938
Churn_Modelling_correlation_heatmap.png,Removing variable Balance might improve the training of decision trees .,1939
Churn_Modelling_correlation_heatmap.png,Removing variable NumOfProducts might improve the training of decision trees .,1940
Churn_Modelling_correlation_heatmap.png,Removing variable EstimatedSalary might improve the training of decision trees .,1941
Churn_Modelling_histograms.png,"Not knowing the semantics of CreditScore variable, dummification could have been a more adequate codification.",1942
Churn_Modelling_histograms.png,"Not knowing the semantics of Geography variable, dummification could have been a more adequate codification.",1943
Churn_Modelling_histograms.png,"Not knowing the semantics of Gender variable, dummification could have been a more adequate codification.",1944
Churn_Modelling_histograms.png,"Not knowing the semantics of Age variable, dummification could have been a more adequate codification.",1945
Churn_Modelling_histograms.png,"Not knowing the semantics of Tenure variable, dummification could have been a more adequate codification.",1946
Churn_Modelling_histograms.png,"Not knowing the semantics of Balance variable, dummification could have been a more adequate codification.",1947
Churn_Modelling_histograms.png,"Not knowing the semantics of NumOfProducts variable, dummification could have been a more adequate codification.",1948
Churn_Modelling_histograms.png,"Not knowing the semantics of HasCrCard variable, dummification could have been a more adequate codification.",1949
Churn_Modelling_histograms.png,"Not knowing the semantics of IsActiveMember variable, dummification could have been a more adequate codification.",1950
Churn_Modelling_histograms.png,"Not knowing the semantics of EstimatedSalary variable, dummification could have been a more adequate codification.",1951
Churn_Modelling_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,1952
Churn_Modelling_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",1953
Churn_Modelling_histograms.png,"Given the usual semantics of CreditScore variable, dummification would have been a better codification.",1954
Churn_Modelling_histograms.png,"Given the usual semantics of Geography variable, dummification would have been a better codification.",1955
Churn_Modelling_histograms.png,"Given the usual semantics of Gender variable, dummification would have been a better codification.",1956
Churn_Modelling_histograms.png,"Given the usual semantics of Age variable, dummification would have been a better codification.",1957
Churn_Modelling_histograms.png,"Given the usual semantics of Tenure variable, dummification would have been a better codification.",1958
Churn_Modelling_histograms.png,"Given the usual semantics of Balance variable, dummification would have been a better codification.",1959
Churn_Modelling_histograms.png,"Given the usual semantics of NumOfProducts variable, dummification would have been a better codification.",1960
Churn_Modelling_histograms.png,"Given the usual semantics of HasCrCard variable, dummification would have been a better codification.",1961
Churn_Modelling_histograms.png,"Given the usual semantics of IsActiveMember variable, dummification would have been a better codification.",1962
Churn_Modelling_histograms.png,"Given the usual semantics of EstimatedSalary variable, dummification would have been a better codification.",1963
Churn_Modelling_histograms.png,The variable CreditScore can be coded as ordinal without losing information.,1964
Churn_Modelling_histograms.png,The variable Geography can be coded as ordinal without losing information.,1965
Churn_Modelling_histograms.png,The variable Gender can be coded as ordinal without losing information.,1966
Churn_Modelling_histograms.png,The variable Age can be coded as ordinal without losing information.,1967
Churn_Modelling_histograms.png,The variable Tenure can be coded as ordinal without losing information.,1968
Churn_Modelling_histograms.png,The variable Balance can be coded as ordinal without losing information.,1969
Churn_Modelling_histograms.png,The variable NumOfProducts can be coded as ordinal without losing information.,1970
Churn_Modelling_histograms.png,The variable HasCrCard can be coded as ordinal without losing information.,1971
Churn_Modelling_histograms.png,The variable IsActiveMember can be coded as ordinal without losing information.,1972
Churn_Modelling_histograms.png,The variable EstimatedSalary can be coded as ordinal without losing information.,1973
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore variable, dummification would be the most adequate encoding.",1974
Churn_Modelling_histograms.png,"Considering the common semantics for Geography variable, dummification would be the most adequate encoding.",1975
Churn_Modelling_histograms.png,"Considering the common semantics for Gender variable, dummification would be the most adequate encoding.",1976
Churn_Modelling_histograms.png,"Considering the common semantics for Age variable, dummification would be the most adequate encoding.",1977
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure variable, dummification would be the most adequate encoding.",1978
Churn_Modelling_histograms.png,"Considering the common semantics for Balance variable, dummification would be the most adequate encoding.",1979
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts variable, dummification would be the most adequate encoding.",1980
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard variable, dummification would be the most adequate encoding.",1981
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember variable, dummification would be the most adequate encoding.",1982
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary variable, dummification would be the most adequate encoding.",1983
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1984
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1985
Churn_Modelling_histograms.png,"Considering the common semantics for Age and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1986
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1987
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1988
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1989
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1990
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1991
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1992
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1993
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1994
Churn_Modelling_histograms.png,"Considering the common semantics for Age and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1995
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1996
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1997
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1998
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1999
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2000
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2001
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2002
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2003
Churn_Modelling_histograms.png,"Considering the common semantics for Age and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2004
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2005
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2006
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2007
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2008
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2009
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2010
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2011
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2012
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2013
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2014
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2015
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2016
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2017
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2018
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2019
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2020
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2021
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2022
Churn_Modelling_histograms.png,"Considering the common semantics for Age and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2023
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2024
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2025
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2026
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2027
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2028
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2029
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2030
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2031
Churn_Modelling_histograms.png,"Considering the common semantics for Age and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2032
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2033
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2034
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2035
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2036
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2037
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2038
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2039
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2040
Churn_Modelling_histograms.png,"Considering the common semantics for Age and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2041
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2042
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2043
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2044
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2045
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2046
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2047
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2048
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2049
Churn_Modelling_histograms.png,"Considering the common semantics for Age and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2050
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2051
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2052
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2053
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2054
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2055
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2056
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2057
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2058
Churn_Modelling_histograms.png,"Considering the common semantics for Age and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2059
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2060
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2061
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2062
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2063
Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2064
Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2065
Churn_Modelling_histograms.png,"Considering the common semantics for Geography and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2066
Churn_Modelling_histograms.png,"Considering the common semantics for Gender and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2067
Churn_Modelling_histograms.png,"Considering the common semantics for Age and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2068
Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2069
Churn_Modelling_histograms.png,"Considering the common semantics for Balance and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2070
Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2071
Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2072
Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2073
Churn_Modelling_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,2074
Churn_Modelling_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,2075
Churn_Modelling_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,2076
Churn_Modelling_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",2077
Churn_Modelling_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",2078
Churn_Modelling_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",2079
Churn_Modelling_correlation_heatmap.png,Variables Age and CreditScore seem to be useful for classification tasks.,2080
Churn_Modelling_correlation_heatmap.png,Variables Tenure and CreditScore seem to be useful for classification tasks.,2081
Churn_Modelling_correlation_heatmap.png,Variables Balance and CreditScore seem to be useful for classification tasks.,2082
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and CreditScore seem to be useful for classification tasks.,2083
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and CreditScore seem to be useful for classification tasks.,2084
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Age seem to be useful for classification tasks.,2085
Churn_Modelling_correlation_heatmap.png,Variables Tenure and Age seem to be useful for classification tasks.,2086
Churn_Modelling_correlation_heatmap.png,Variables Balance and Age seem to be useful for classification tasks.,2087
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Age seem to be useful for classification tasks.,2088
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and Age seem to be useful for classification tasks.,2089
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Tenure seem to be useful for classification tasks.,2090
Churn_Modelling_correlation_heatmap.png,Variables Age and Tenure seem to be useful for classification tasks.,2091
Churn_Modelling_correlation_heatmap.png,Variables Balance and Tenure seem to be useful for classification tasks.,2092
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Tenure seem to be useful for classification tasks.,2093
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and Tenure seem to be useful for classification tasks.,2094
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Balance seem to be useful for classification tasks.,2095
Churn_Modelling_correlation_heatmap.png,Variables Age and Balance seem to be useful for classification tasks.,2096
Churn_Modelling_correlation_heatmap.png,Variables Tenure and Balance seem to be useful for classification tasks.,2097
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Balance seem to be useful for classification tasks.,2098
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and Balance seem to be useful for classification tasks.,2099
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and NumOfProducts seem to be useful for classification tasks.,2100
Churn_Modelling_correlation_heatmap.png,Variables Age and NumOfProducts seem to be useful for classification tasks.,2101
Churn_Modelling_correlation_heatmap.png,Variables Tenure and NumOfProducts seem to be useful for classification tasks.,2102
Churn_Modelling_correlation_heatmap.png,Variables Balance and NumOfProducts seem to be useful for classification tasks.,2103
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and NumOfProducts seem to be useful for classification tasks.,2104
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and EstimatedSalary seem to be useful for classification tasks.,2105
Churn_Modelling_correlation_heatmap.png,Variables Age and EstimatedSalary seem to be useful for classification tasks.,2106
Churn_Modelling_correlation_heatmap.png,Variables Tenure and EstimatedSalary seem to be useful for classification tasks.,2107
Churn_Modelling_correlation_heatmap.png,Variables Balance and EstimatedSalary seem to be useful for classification tasks.,2108
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and EstimatedSalary seem to be useful for classification tasks.,2109
Churn_Modelling_correlation_heatmap.png,Variable CreditScore seems to be relevant for the majority of mining tasks.,2110
Churn_Modelling_correlation_heatmap.png,Variable Age seems to be relevant for the majority of mining tasks.,2111
Churn_Modelling_correlation_heatmap.png,Variable Tenure seems to be relevant for the majority of mining tasks.,2112
Churn_Modelling_correlation_heatmap.png,Variable Balance seems to be relevant for the majority of mining tasks.,2113
Churn_Modelling_correlation_heatmap.png,Variable NumOfProducts seems to be relevant for the majority of mining tasks.,2114
Churn_Modelling_correlation_heatmap.png,Variable EstimatedSalary seems to be relevant for the majority of mining tasks.,2115
Churn_Modelling_decision_tree.png,Variable CreditScore is one of the most relevant variables.,2116
Churn_Modelling_decision_tree.png,Variable Age is one of the most relevant variables.,2117
Churn_Modelling_decision_tree.png,Variable Tenure is one of the most relevant variables.,2118
Churn_Modelling_decision_tree.png,Variable Balance is one of the most relevant variables.,2119
Churn_Modelling_decision_tree.png,Variable NumOfProducts is one of the most relevant variables.,2120
Churn_Modelling_decision_tree.png,Variable EstimatedSalary is one of the most relevant variables.,2121
Churn_Modelling_decision_tree.png,It is possible to state that CreditScore is the first most discriminative variable regarding the class.,2122
Churn_Modelling_decision_tree.png,It is possible to state that Age is the first most discriminative variable regarding the class.,2123
Churn_Modelling_decision_tree.png,It is possible to state that Tenure is the first most discriminative variable regarding the class.,2124
Churn_Modelling_decision_tree.png,It is possible to state that Balance is the first most discriminative variable regarding the class.,2125
Churn_Modelling_decision_tree.png,It is possible to state that NumOfProducts is the first most discriminative variable regarding the class.,2126
Churn_Modelling_decision_tree.png,It is possible to state that EstimatedSalary is the first most discriminative variable regarding the class.,2127
Churn_Modelling_decision_tree.png,It is possible to state that CreditScore is the second most discriminative variable regarding the class.,2128
Churn_Modelling_decision_tree.png,It is possible to state that Age is the second most discriminative variable regarding the class.,2129
Churn_Modelling_decision_tree.png,It is possible to state that Tenure is the second most discriminative variable regarding the class.,2130
Churn_Modelling_decision_tree.png,It is possible to state that Balance is the second most discriminative variable regarding the class.,2131
Churn_Modelling_decision_tree.png,It is possible to state that NumOfProducts is the second most discriminative variable regarding the class.,2132
Churn_Modelling_decision_tree.png,It is possible to state that EstimatedSalary is the second most discriminative variable regarding the class.,2133
Churn_Modelling_decision_tree.png,"The variable CreditScore discriminates between the target values, as shown in the decision tree.",2134
Churn_Modelling_decision_tree.png,"The variable Age discriminates between the target values, as shown in the decision tree.",2135
Churn_Modelling_decision_tree.png,"The variable Tenure discriminates between the target values, as shown in the decision tree.",2136
Churn_Modelling_decision_tree.png,"The variable Balance discriminates between the target values, as shown in the decision tree.",2137
Churn_Modelling_decision_tree.png,"The variable NumOfProducts discriminates between the target values, as shown in the decision tree.",2138
Churn_Modelling_decision_tree.png,"The variable EstimatedSalary discriminates between the target values, as shown in the decision tree.",2139
Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the two most relevant features.,2140
Churn_Modelling_decision_tree.png,The variable Age seems to be one of the two most relevant features.,2141
Churn_Modelling_decision_tree.png,The variable Tenure seems to be one of the two most relevant features.,2142
Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the two most relevant features.,2143
Churn_Modelling_decision_tree.png,The variable NumOfProducts seems to be one of the two most relevant features.,2144
Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the two most relevant features.,2145
Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the three most relevant features.,2146
Churn_Modelling_decision_tree.png,The variable Age seems to be one of the three most relevant features.,2147
Churn_Modelling_decision_tree.png,The variable Tenure seems to be one of the three most relevant features.,2148
Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the three most relevant features.,2149
Churn_Modelling_decision_tree.png,The variable NumOfProducts seems to be one of the three most relevant features.,2150
Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the three most relevant features.,2151
Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the four most relevant features.,2152
Churn_Modelling_decision_tree.png,The variable Age seems to be one of the four most relevant features.,2153
Churn_Modelling_decision_tree.png,The variable Tenure seems to be one of the four most relevant features.,2154
Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the four most relevant features.,2155
Churn_Modelling_decision_tree.png,The variable NumOfProducts seems to be one of the four most relevant features.,2156
Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the four most relevant features.,2157
Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the five most relevant features.,2158
Churn_Modelling_decision_tree.png,The variable Age seems to be one of the five most relevant features.,2159
Churn_Modelling_decision_tree.png,The variable Tenure seems to be one of the five most relevant features.,2160
Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the five most relevant features.,2161
Churn_Modelling_decision_tree.png,The variable NumOfProducts seems to be one of the five most relevant features.,2162
Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the five most relevant features.,2163
Churn_Modelling_decision_tree.png,It is clear that variable CreditScore is one of the two most relevant features.,2164
Churn_Modelling_decision_tree.png,It is clear that variable Age is one of the two most relevant features.,2165
Churn_Modelling_decision_tree.png,It is clear that variable Tenure is one of the two most relevant features.,2166
Churn_Modelling_decision_tree.png,It is clear that variable Balance is one of the two most relevant features.,2167
Churn_Modelling_decision_tree.png,It is clear that variable NumOfProducts is one of the two most relevant features.,2168
Churn_Modelling_decision_tree.png,It is clear that variable EstimatedSalary is one of the two most relevant features.,2169
Churn_Modelling_decision_tree.png,It is clear that variable CreditScore is one of the three most relevant features.,2170
Churn_Modelling_decision_tree.png,It is clear that variable Age is one of the three most relevant features.,2171
Churn_Modelling_decision_tree.png,It is clear that variable Tenure is one of the three most relevant features.,2172
Churn_Modelling_decision_tree.png,It is clear that variable Balance is one of the three most relevant features.,2173
Churn_Modelling_decision_tree.png,It is clear that variable NumOfProducts is one of the three most relevant features.,2174
Churn_Modelling_decision_tree.png,It is clear that variable EstimatedSalary is one of the three most relevant features.,2175
Churn_Modelling_decision_tree.png,It is clear that variable CreditScore is one of the four most relevant features.,2176
Churn_Modelling_decision_tree.png,It is clear that variable Age is one of the four most relevant features.,2177
Churn_Modelling_decision_tree.png,It is clear that variable Tenure is one of the four most relevant features.,2178
Churn_Modelling_decision_tree.png,It is clear that variable Balance is one of the four most relevant features.,2179
Churn_Modelling_decision_tree.png,It is clear that variable NumOfProducts is one of the four most relevant features.,2180
Churn_Modelling_decision_tree.png,It is clear that variable EstimatedSalary is one of the four most relevant features.,2181
Churn_Modelling_decision_tree.png,It is clear that variable CreditScore is one of the five most relevant features.,2182
Churn_Modelling_decision_tree.png,It is clear that variable Age is one of the five most relevant features.,2183
Churn_Modelling_decision_tree.png,It is clear that variable Tenure is one of the five most relevant features.,2184
Churn_Modelling_decision_tree.png,It is clear that variable Balance is one of the five most relevant features.,2185
Churn_Modelling_decision_tree.png,It is clear that variable NumOfProducts is one of the five most relevant features.,2186
Churn_Modelling_decision_tree.png,It is clear that variable EstimatedSalary is one of the five most relevant features.,2187
Churn_Modelling_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",2188
Churn_Modelling_correlation_heatmap.png,Variables Age and CreditScore are redundant.,2189
Churn_Modelling_correlation_heatmap.png,Variables Tenure and CreditScore are redundant.,2190
Churn_Modelling_correlation_heatmap.png,Variables Balance and CreditScore are redundant.,2191
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and CreditScore are redundant.,2192
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and CreditScore are redundant.,2193
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Age are redundant.,2194
Churn_Modelling_correlation_heatmap.png,Variables Tenure and Age are redundant.,2195
Churn_Modelling_correlation_heatmap.png,Variables Balance and Age are redundant.,2196
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Age are redundant.,2197
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and Age are redundant.,2198
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Tenure are redundant.,2199
Churn_Modelling_correlation_heatmap.png,Variables Age and Tenure are redundant.,2200
Churn_Modelling_correlation_heatmap.png,Variables Balance and Tenure are redundant.,2201
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Tenure are redundant.,2202
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and Tenure are redundant.,2203
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Balance are redundant.,2204
Churn_Modelling_correlation_heatmap.png,Variables Age and Balance are redundant.,2205
Churn_Modelling_correlation_heatmap.png,Variables Tenure and Balance are redundant.,2206
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Balance are redundant.,2207
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and Balance are redundant.,2208
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and NumOfProducts are redundant.,2209
Churn_Modelling_correlation_heatmap.png,Variables Age and NumOfProducts are redundant.,2210
Churn_Modelling_correlation_heatmap.png,Variables Tenure and NumOfProducts are redundant.,2211
Churn_Modelling_correlation_heatmap.png,Variables Balance and NumOfProducts are redundant.,2212
Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and NumOfProducts are redundant.,2213
Churn_Modelling_correlation_heatmap.png,Variables CreditScore and EstimatedSalary are redundant.,2214
Churn_Modelling_correlation_heatmap.png,Variables Age and EstimatedSalary are redundant.,2215
Churn_Modelling_correlation_heatmap.png,Variables Tenure and EstimatedSalary are redundant.,2216
Churn_Modelling_correlation_heatmap.png,Variables Balance and EstimatedSalary are redundant.,2217
Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and EstimatedSalary are redundant.,2218
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and NumOfProducts are redundant, but we can’t say the same for the pair Age and IsActiveMember.",2219
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and EstimatedSalary are redundant, but we can’t say the same for the pair Balance and NumOfProducts.",2220
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and IsActiveMember are redundant, but we can’t say the same for the pair Gender and Age.",2221
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and NumOfProducts are redundant, but we can’t say the same for the pair Geography and IsActiveMember.",2222
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and Balance.",2223
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and EstimatedSalary are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2224
Churn_Modelling_correlation_heatmap.png,"Variables Age and HasCrCard are redundant, but we can’t say the same for the pair CreditScore and IsActiveMember.",2225
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Gender are redundant, but we can’t say the same for the pair Geography and IsActiveMember.",2226
Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair Gender and Tenure.",2227
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Tenure are redundant, but we can’t say the same for the pair NumOfProducts and IsActiveMember.",2228
Churn_Modelling_correlation_heatmap.png,"Variables Balance and NumOfProducts are redundant, but we can’t say the same for the pair Tenure and HasCrCard.",2229
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and HasCrCard are redundant, but we can’t say the same for the pair Gender and Age.",2230
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and IsActiveMember are redundant, but we can’t say the same for the pair HasCrCard and EstimatedSalary.",2231
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Age are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2232
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair CreditScore and Age.",2233
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and EstimatedSalary are redundant, but we can’t say the same for the pair Geography and IsActiveMember.",2234
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and NumOfProducts.",2235
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Geography are redundant, but we can’t say the same for the pair Tenure and HasCrCard.",2236
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and HasCrCard are redundant, but we can’t say the same for the pair Geography and Age.",2237
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and IsActiveMember are redundant, but we can’t say the same for the pair Geography and Age.",2238
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and IsActiveMember are redundant, but we can’t say the same for the pair CreditScore and Geography.",2239
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Tenure are redundant, but we can’t say the same for the pair Balance and EstimatedSalary.",2240
Churn_Modelling_correlation_heatmap.png,"Variables Age and HasCrCard are redundant, but we can’t say the same for the pair Gender and Balance.",2241
Churn_Modelling_correlation_heatmap.png,"Variables Age and Balance are redundant, but we can’t say the same for the pair Geography and EstimatedSalary.",2242
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair Gender and HasCrCard.",2243
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and HasCrCard are redundant, but we can’t say the same for the pair NumOfProducts and EstimatedSalary.",2244
Churn_Modelling_correlation_heatmap.png,"Variables IsActiveMember and EstimatedSalary are redundant, but we can’t say the same for the pair Age and Balance.",2245
Churn_Modelling_correlation_heatmap.png,"Variables HasCrCard and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and Gender.",2246
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and HasCrCard are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2247
Churn_Modelling_correlation_heatmap.png,"Variables Age and HasCrCard are redundant, but we can’t say the same for the pair Gender and Tenure.",2248
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and NumOfProducts are redundant, but we can’t say the same for the pair IsActiveMember and EstimatedSalary.",2249
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Balance are redundant, but we can’t say the same for the pair CreditScore and IsActiveMember.",2250
Churn_Modelling_correlation_heatmap.png,"Variables Age and IsActiveMember are redundant, but we can’t say the same for the pair Gender and EstimatedSalary.",2251
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and HasCrCard are redundant, but we can’t say the same for the pair Gender and Balance.",2252
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and EstimatedSalary are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2253
Churn_Modelling_correlation_heatmap.png,"Variables Age and Balance are redundant, but we can’t say the same for the pair Tenure and NumOfProducts.",2254
Churn_Modelling_correlation_heatmap.png,"Variables Gender and HasCrCard are redundant, but we can’t say the same for the pair Age and IsActiveMember.",2255
Churn_Modelling_correlation_heatmap.png,"Variables Balance and IsActiveMember are redundant, but we can’t say the same for the pair CreditScore and Tenure.",2256
Churn_Modelling_correlation_heatmap.png,"Variables HasCrCard and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and IsActiveMember.",2257
Churn_Modelling_correlation_heatmap.png,"Variables Age and NumOfProducts are redundant, but we can’t say the same for the pair CreditScore and Gender.",2258
Churn_Modelling_correlation_heatmap.png,"Variables Balance and HasCrCard are redundant, but we can’t say the same for the pair Age and Tenure.",2259
Churn_Modelling_correlation_heatmap.png,"Variables Age and HasCrCard are redundant, but we can’t say the same for the pair Geography and NumOfProducts.",2260
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and EstimatedSalary are redundant, but we can’t say the same for the pair Geography and Gender.",2261
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and IsActiveMember are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2262
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Gender are redundant, but we can’t say the same for the pair Geography and EstimatedSalary.",2263
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Tenure are redundant, but we can’t say the same for the pair Gender and Age.",2264
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Geography are redundant, but we can’t say the same for the pair Age and NumOfProducts.",2265
Churn_Modelling_correlation_heatmap.png,"Variables HasCrCard and IsActiveMember are redundant, but we can’t say the same for the pair Gender and Tenure.",2266
Churn_Modelling_correlation_heatmap.png,"Variables Balance and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and NumOfProducts.",2267
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and IsActiveMember are redundant, but we can’t say the same for the pair Geography and Gender.",2268
Churn_Modelling_correlation_heatmap.png,"Variables Age and IsActiveMember are redundant, but we can’t say the same for the pair CreditScore and HasCrCard.",2269
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and Balance are redundant, but we can’t say the same for the pair Geography and NumOfProducts.",2270
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Gender are redundant, but we can’t say the same for the pair CreditScore and HasCrCard.",2271
Churn_Modelling_correlation_heatmap.png,"Variables Gender and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and HasCrCard.",2272
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Age are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2273
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Age are redundant, but we can’t say the same for the pair Geography and Tenure.",2274
Churn_Modelling_correlation_heatmap.png,"Variables Gender and IsActiveMember are redundant, but we can’t say the same for the pair Geography and Balance.",2275
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair Age and HasCrCard.",2276
Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair Balance and EstimatedSalary.",2277
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Age are redundant, but we can’t say the same for the pair Tenure and HasCrCard.",2278
Churn_Modelling_correlation_heatmap.png,"Variables Balance and IsActiveMember are redundant, but we can’t say the same for the pair CreditScore and Geography.",2279
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Balance are redundant, but we can’t say the same for the pair Tenure and EstimatedSalary.",2280
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Age are redundant, but we can’t say the same for the pair Tenure and Balance.",2281
Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair NumOfProducts and IsActiveMember.",2282
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair Balance and NumOfProducts.",2283
Churn_Modelling_correlation_heatmap.png,"Variables Age and Tenure are redundant, but we can’t say the same for the pair CreditScore and Balance.",2284
Churn_Modelling_correlation_heatmap.png,"Variables Gender and EstimatedSalary are redundant, but we can’t say the same for the pair Geography and HasCrCard.",2285
Churn_Modelling_correlation_heatmap.png,"Variables Age and NumOfProducts are redundant, but we can’t say the same for the pair Gender and Balance.",2286
Churn_Modelling_correlation_heatmap.png,"Variables Gender and EstimatedSalary are redundant, but we can’t say the same for the pair Tenure and HasCrCard.",2287
Churn_Modelling_correlation_heatmap.png,"Variables Age and Tenure are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2288
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Tenure are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2289
Churn_Modelling_correlation_heatmap.png,"Variables Balance and NumOfProducts are redundant, but we can’t say the same for the pair HasCrCard and IsActiveMember.",2290
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair Gender and Tenure.",2291
Churn_Modelling_correlation_heatmap.png,"Variables Balance and EstimatedSalary are redundant, but we can’t say the same for the pair Age and NumOfProducts.",2292
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Balance are redundant, but we can’t say the same for the pair CreditScore and Gender.",2293
Churn_Modelling_correlation_heatmap.png,"Variables Age and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and Geography.",2294
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Balance are redundant, but we can’t say the same for the pair CreditScore and Tenure.",2295
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair Balance and EstimatedSalary.",2296
Churn_Modelling_correlation_heatmap.png,"Variables HasCrCard and EstimatedSalary are redundant, but we can’t say the same for the pair Geography and IsActiveMember.",2297
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and NumOfProducts are redundant, but we can’t say the same for the pair HasCrCard and EstimatedSalary.",2298
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and EstimatedSalary are redundant, but we can’t say the same for the pair Geography and NumOfProducts.",2299
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair CreditScore and IsActiveMember.",2300
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and NumOfProducts are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2301
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair Age and IsActiveMember.",2302
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair Age and Tenure.",2303
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Age are redundant, but we can’t say the same for the pair Tenure and Balance.",2304
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair Gender and Age.",2305
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Balance are redundant, but we can’t say the same for the pair HasCrCard and EstimatedSalary.",2306
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Tenure are redundant, but we can’t say the same for the pair CreditScore and Geography.",2307
Churn_Modelling_correlation_heatmap.png,"Variables Gender and HasCrCard are redundant, but we can’t say the same for the pair CreditScore and EstimatedSalary.",2308
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and Gender.",2309
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and HasCrCard are redundant, but we can’t say the same for the pair Age and Balance.",2310
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Age are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2311
Churn_Modelling_correlation_heatmap.png,"Variables Gender and HasCrCard are redundant, but we can’t say the same for the pair Geography and EstimatedSalary.",2312
Churn_Modelling_correlation_heatmap.png,"Variables Gender and NumOfProducts are redundant, but we can’t say the same for the pair Tenure and EstimatedSalary.",2313
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and HasCrCard are redundant, but we can’t say the same for the pair Age and IsActiveMember.",2314
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Age are redundant, but we can’t say the same for the pair Geography and HasCrCard.",2315
Churn_Modelling_correlation_heatmap.png,"Variables Balance and NumOfProducts are redundant, but we can’t say the same for the pair Gender and Age.",2316
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Age are redundant, but we can’t say the same for the pair HasCrCard and IsActiveMember.",2317
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Gender are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2318
Churn_Modelling_correlation_heatmap.png,"Variables Age and IsActiveMember are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2319
Churn_Modelling_correlation_heatmap.png,"Variables Age and EstimatedSalary are redundant, but we can’t say the same for the pair Gender and Balance.",2320
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and HasCrCard are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2321
Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair CreditScore and Gender.",2322
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2323
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair Geography and Gender.",2324
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Geography are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2325
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Balance are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2326
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and EstimatedSalary are redundant, but we can’t say the same for the pair Age and NumOfProducts.",2327
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair Tenure and EstimatedSalary.",2328
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and EstimatedSalary are redundant, but we can’t say the same for the pair Gender and Age.",2329
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and IsActiveMember are redundant, but we can’t say the same for the pair Gender and Age.",2330
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Balance are redundant, but we can’t say the same for the pair NumOfProducts and EstimatedSalary.",2331
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and HasCrCard are redundant, but we can’t say the same for the pair Tenure and EstimatedSalary.",2332
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair Age and Balance.",2333
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and NumOfProducts are redundant, but we can’t say the same for the pair Tenure and HasCrCard.",2334
Churn_Modelling_correlation_heatmap.png,"Variables Balance and EstimatedSalary are redundant, but we can’t say the same for the pair Geography and NumOfProducts.",2335
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Age are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2336
Churn_Modelling_correlation_heatmap.png,"Variables Balance and HasCrCard are redundant, but we can’t say the same for the pair Gender and EstimatedSalary.",2337
Churn_Modelling_correlation_heatmap.png,"Variables Age and IsActiveMember are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2338
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Balance are redundant, but we can’t say the same for the pair NumOfProducts and IsActiveMember.",2339
Churn_Modelling_correlation_heatmap.png,"Variables Gender and EstimatedSalary are redundant, but we can’t say the same for the pair Age and NumOfProducts.",2340
Churn_Modelling_correlation_heatmap.png,"Variables Gender and NumOfProducts are redundant, but we can’t say the same for the pair CreditScore and HasCrCard.",2341
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Balance are redundant, but we can’t say the same for the pair NumOfProducts and EstimatedSalary.",2342
Churn_Modelling_correlation_heatmap.png,"Variables Balance and NumOfProducts are redundant, but we can’t say the same for the pair HasCrCard and EstimatedSalary.",2343
Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair Tenure and NumOfProducts.",2344
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and HasCrCard are redundant, but we can’t say the same for the pair Geography and Gender.",2345
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2346
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair CreditScore and Balance.",2347
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and IsActiveMember are redundant, but we can’t say the same for the pair Age and EstimatedSalary.",2348
Churn_Modelling_correlation_heatmap.png,"Variables HasCrCard and IsActiveMember are redundant, but we can’t say the same for the pair Geography and Age.",2349
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and Balance are redundant, but we can’t say the same for the pair CreditScore and Geography.",2350
Churn_Modelling_correlation_heatmap.png,"Variables Gender and IsActiveMember are redundant, but we can’t say the same for the pair Geography and EstimatedSalary.",2351
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Balance are redundant, but we can’t say the same for the pair CreditScore and Tenure.",2352
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and HasCrCard are redundant, but we can’t say the same for the pair Tenure and Balance.",2353
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair Gender and EstimatedSalary.",2354
Churn_Modelling_correlation_heatmap.png,"Variables Age and IsActiveMember are redundant, but we can’t say the same for the pair Geography and HasCrCard.",2355
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and NumOfProducts are redundant, but we can’t say the same for the pair CreditScore and Geography.",2356
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair HasCrCard and EstimatedSalary.",2357
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair CreditScore and Age.",2358
Churn_Modelling_correlation_heatmap.png,"Variables Gender and NumOfProducts are redundant, but we can’t say the same for the pair Age and EstimatedSalary.",2359
Churn_Modelling_correlation_heatmap.png,"Variables Age and NumOfProducts are redundant, but we can’t say the same for the pair Gender and Tenure.",2360
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Age are redundant, but we can’t say the same for the pair IsActiveMember and EstimatedSalary.",2361
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Age are redundant, but we can’t say the same for the pair Balance and NumOfProducts.",2362
Churn_Modelling_correlation_heatmap.png,"Variables Age and NumOfProducts are redundant, but we can’t say the same for the pair Gender and IsActiveMember.",2363
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Age are redundant, but we can’t say the same for the pair NumOfProducts and EstimatedSalary.",2364
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and Balance are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2365
Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair CreditScore and EstimatedSalary.",2366
Churn_Modelling_correlation_heatmap.png,"Variables Balance and EstimatedSalary are redundant, but we can’t say the same for the pair Gender and Age.",2367
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and HasCrCard are redundant, but we can’t say the same for the pair Geography and NumOfProducts.",2368
Churn_Modelling_correlation_heatmap.png,"Variables Gender and NumOfProducts are redundant, but we can’t say the same for the pair Tenure and Balance.",2369
Churn_Modelling_correlation_heatmap.png,"Variables HasCrCard and EstimatedSalary are redundant, but we can’t say the same for the pair Age and Tenure.",2370
Churn_Modelling_correlation_heatmap.png,"Variables Gender and HasCrCard are redundant, but we can’t say the same for the pair IsActiveMember and EstimatedSalary.",2371
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair Balance and EstimatedSalary.",2372
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and Balance are redundant, but we can’t say the same for the pair CreditScore and Gender.",2373
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Gender are redundant, but we can’t say the same for the pair Tenure and NumOfProducts.",2374
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and IsActiveMember are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2375
Churn_Modelling_correlation_heatmap.png,"Variables Geography and Tenure are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2376
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and HasCrCard are redundant, but we can’t say the same for the pair Age and Balance.",2377
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and NumOfProducts are redundant, but we can’t say the same for the pair Age and Tenure.",2378
Churn_Modelling_correlation_heatmap.png,"Variables Age and IsActiveMember are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2379
Churn_Modelling_correlation_heatmap.png,"Variables Geography and EstimatedSalary are redundant, but we can’t say the same for the pair Gender and Tenure.",2380
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and IsActiveMember are redundant, but we can’t say the same for the pair Age and HasCrCard.",2381
Churn_Modelling_correlation_heatmap.png,"Variables Age and NumOfProducts are redundant, but we can’t say the same for the pair Tenure and Balance.",2382
Churn_Modelling_correlation_heatmap.png,"Variables Age and HasCrCard are redundant, but we can’t say the same for the pair CreditScore and Geography.",2383
Churn_Modelling_correlation_heatmap.png,"Variables Geography and EstimatedSalary are redundant, but we can’t say the same for the pair Age and HasCrCard.",2384
Churn_Modelling_correlation_heatmap.png,"Variables Tenure and Balance are redundant, but we can’t say the same for the pair NumOfProducts and EstimatedSalary.",2385
Churn_Modelling_correlation_heatmap.png,"Variables Gender and Balance are redundant, but we can’t say the same for the pair Geography and HasCrCard.",2386
Churn_Modelling_correlation_heatmap.png,"Variables Geography and IsActiveMember are redundant, but we can’t say the same for the pair Gender and Tenure.",2387
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Geography are redundant, but we can’t say the same for the pair Gender and Tenure.",2388
Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair CreditScore and Gender.",2389
Churn_Modelling_correlation_heatmap.png,"Variables Gender and EstimatedSalary are redundant, but we can’t say the same for the pair HasCrCard and IsActiveMember.",2390
Churn_Modelling_correlation_heatmap.png,"Variables Geography and EstimatedSalary are redundant, but we can’t say the same for the pair Age and IsActiveMember.",2391
Churn_Modelling_correlation_heatmap.png,"Variables Age and IsActiveMember are redundant, but we can’t say the same for the pair CreditScore and Balance.",2392
Churn_Modelling_correlation_heatmap.png,"Variables Age and EstimatedSalary are redundant, but we can’t say the same for the pair CreditScore and Tenure.",2393
Churn_Modelling_correlation_heatmap.png,"Variables HasCrCard and IsActiveMember are redundant, but we can’t say the same for the pair Gender and Age.",2394
Churn_Modelling_correlation_heatmap.png,"Variables Gender and IsActiveMember are redundant, but we can’t say the same for the pair Tenure and HasCrCard.",2395
Churn_Modelling_correlation_heatmap.png,"Variables NumOfProducts and IsActiveMember are redundant, but we can’t say the same for the pair Age and HasCrCard.",2396
Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2397
Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair Gender and Age.",2398
Churn_Modelling_correlation_heatmap.png,The variable CreditScore can be discarded without risking losing information.,2399
Churn_Modelling_correlation_heatmap.png,The variable Age can be discarded without risking losing information.,2400
Churn_Modelling_correlation_heatmap.png,The variable Tenure can be discarded without risking losing information.,2401
Churn_Modelling_correlation_heatmap.png,The variable Balance can be discarded without risking losing information.,2402
Churn_Modelling_correlation_heatmap.png,The variable NumOfProducts can be discarded without risking losing information.,2403
Churn_Modelling_correlation_heatmap.png,The variable EstimatedSalary can be discarded without risking losing information.,2404
Churn_Modelling_correlation_heatmap.png,One of the variables Age or CreditScore can be discarded without losing information.,2405
Churn_Modelling_correlation_heatmap.png,One of the variables Tenure or CreditScore can be discarded without losing information.,2406
Churn_Modelling_correlation_heatmap.png,One of the variables Balance or CreditScore can be discarded without losing information.,2407
Churn_Modelling_correlation_heatmap.png,One of the variables NumOfProducts or CreditScore can be discarded without losing information.,2408
Churn_Modelling_correlation_heatmap.png,One of the variables EstimatedSalary or CreditScore can be discarded without losing information.,2409
Churn_Modelling_correlation_heatmap.png,One of the variables CreditScore or Age can be discarded without losing information.,2410
Churn_Modelling_correlation_heatmap.png,One of the variables Tenure or Age can be discarded without losing information.,2411
Churn_Modelling_correlation_heatmap.png,One of the variables Balance or Age can be discarded without losing information.,2412
Churn_Modelling_correlation_heatmap.png,One of the variables NumOfProducts or Age can be discarded without losing information.,2413
Churn_Modelling_correlation_heatmap.png,One of the variables EstimatedSalary or Age can be discarded without losing information.,2414
Churn_Modelling_correlation_heatmap.png,One of the variables CreditScore or Tenure can be discarded without losing information.,2415
Churn_Modelling_correlation_heatmap.png,One of the variables Age or Tenure can be discarded without losing information.,2416
Churn_Modelling_correlation_heatmap.png,One of the variables Balance or Tenure can be discarded without losing information.,2417
Churn_Modelling_correlation_heatmap.png,One of the variables NumOfProducts or Tenure can be discarded without losing information.,2418
Churn_Modelling_correlation_heatmap.png,One of the variables EstimatedSalary or Tenure can be discarded without losing information.,2419
Churn_Modelling_correlation_heatmap.png,One of the variables CreditScore or Balance can be discarded without losing information.,2420
Churn_Modelling_correlation_heatmap.png,One of the variables Age or Balance can be discarded without losing information.,2421
Churn_Modelling_correlation_heatmap.png,One of the variables Tenure or Balance can be discarded without losing information.,2422
Churn_Modelling_correlation_heatmap.png,One of the variables NumOfProducts or Balance can be discarded without losing information.,2423
Churn_Modelling_correlation_heatmap.png,One of the variables EstimatedSalary or Balance can be discarded without losing information.,2424
Churn_Modelling_correlation_heatmap.png,One of the variables CreditScore or NumOfProducts can be discarded without losing information.,2425
Churn_Modelling_correlation_heatmap.png,One of the variables Age or NumOfProducts can be discarded without losing information.,2426
Churn_Modelling_correlation_heatmap.png,One of the variables Tenure or NumOfProducts can be discarded without losing information.,2427
Churn_Modelling_correlation_heatmap.png,One of the variables Balance or NumOfProducts can be discarded without losing information.,2428
Churn_Modelling_correlation_heatmap.png,One of the variables EstimatedSalary or NumOfProducts can be discarded without losing information.,2429
Churn_Modelling_correlation_heatmap.png,One of the variables CreditScore or EstimatedSalary can be discarded without losing information.,2430
Churn_Modelling_correlation_heatmap.png,One of the variables Age or EstimatedSalary can be discarded without losing information.,2431
Churn_Modelling_correlation_heatmap.png,One of the variables Tenure or EstimatedSalary can be discarded without losing information.,2432
Churn_Modelling_correlation_heatmap.png,One of the variables Balance or EstimatedSalary can be discarded without losing information.,2433
Churn_Modelling_correlation_heatmap.png,One of the variables NumOfProducts or EstimatedSalary can be discarded without losing information.,2434
Churn_Modelling_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,2435
Churn_Modelling_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,2436
Churn_Modelling_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,2437
Churn_Modelling_boxplots.png,At least 50 of the variables present outliers.,2438
Churn_Modelling_boxplots.png,At least 60 of the variables present outliers.,2439
Churn_Modelling_histograms_numeric.png,At least 75 of the variables present outliers.,2440
Churn_Modelling_boxplots.png,At least 85 of the variables present outliers.,2441
Churn_Modelling_boxplots.png,Variable CreditScore presents some outliers.,2442
Churn_Modelling_boxplots.png,Variable Age presents some outliers.,2443
Churn_Modelling_boxplots.png,Variable Tenure presents some outliers.,2444
Churn_Modelling_boxplots.png,Variable Balance presents some outliers.,2445
Churn_Modelling_boxplots.png,Variable NumOfProducts presents some outliers.,2446
Churn_Modelling_boxplots.png,Variable EstimatedSalary presents some outliers.,2447
Churn_Modelling_boxplots.png,Variable CreditScore doesn’t have any outliers.,2448
Churn_Modelling_histograms_numeric.png,Variable Age doesn’t have any outliers.,2449
Churn_Modelling_boxplots.png,Variable Tenure doesn’t have any outliers.,2450
Churn_Modelling_histograms_numeric.png,Variable Balance doesn’t have any outliers.,2451
Churn_Modelling_histograms_numeric.png,Variable NumOfProducts doesn’t have any outliers.,2452
Churn_Modelling_histograms_numeric.png,Variable EstimatedSalary doesn’t have any outliers.,2453
Churn_Modelling_boxplots.png,Variable CreditScore shows some outlier values.,2454
Churn_Modelling_histograms_numeric.png,Variable Age shows some outlier values.,2455
Churn_Modelling_histograms_numeric.png,Variable Tenure shows some outlier values.,2456
Churn_Modelling_histograms_numeric.png,Variable Balance shows some outlier values.,2457
Churn_Modelling_histograms_numeric.png,Variable NumOfProducts shows some outlier values.,2458
Churn_Modelling_boxplots.png,Variable EstimatedSalary shows some outlier values.,2459
Churn_Modelling_histograms_numeric.png,Variable CreditScore shows a high number of outlier values.,2460
Churn_Modelling_histograms_numeric.png,Variable Age shows a high number of outlier values.,2461
Churn_Modelling_histograms_numeric.png,Variable Tenure shows a high number of outlier values.,2462
Churn_Modelling_histograms_numeric.png,Variable Balance shows a high number of outlier values.,2463
Churn_Modelling_histograms_numeric.png,Variable NumOfProducts shows a high number of outlier values.,2464
Churn_Modelling_histograms_numeric.png,Variable EstimatedSalary shows a high number of outlier values.,2465
Churn_Modelling_boxplots.png,Outliers seem to be a problem in the dataset.,2466
Churn_Modelling_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable CreditScore.",2467
Churn_Modelling_boxplots.png,"It is clear that variable Tenure shows some outliers, but we can’t be sure of the same for variable CreditScore.",2468
Churn_Modelling_histograms_numeric.png,"It is clear that variable Balance shows some outliers, but we can’t be sure of the same for variable CreditScore.",2469
Churn_Modelling_boxplots.png,"It is clear that variable NumOfProducts shows some outliers, but we can’t be sure of the same for variable CreditScore.",2470
Churn_Modelling_histograms_numeric.png,"It is clear that variable EstimatedSalary shows some outliers, but we can’t be sure of the same for variable CreditScore.",2471
Churn_Modelling_histograms_numeric.png,"It is clear that variable CreditScore shows some outliers, but we can’t be sure of the same for variable Age.",2472
Churn_Modelling_boxplots.png,"It is clear that variable Tenure shows some outliers, but we can’t be sure of the same for variable Age.",2473
Churn_Modelling_boxplots.png,"It is clear that variable Balance shows some outliers, but we can’t be sure of the same for variable Age.",2474
Churn_Modelling_boxplots.png,"It is clear that variable NumOfProducts shows some outliers, but we can’t be sure of the same for variable Age.",2475
Churn_Modelling_boxplots.png,"It is clear that variable EstimatedSalary shows some outliers, but we can’t be sure of the same for variable Age.",2476
Churn_Modelling_histograms_numeric.png,"It is clear that variable CreditScore shows some outliers, but we can’t be sure of the same for variable Tenure.",2477
Churn_Modelling_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Tenure.",2478
Churn_Modelling_histograms_numeric.png,"It is clear that variable Balance shows some outliers, but we can’t be sure of the same for variable Tenure.",2479
Churn_Modelling_boxplots.png,"It is clear that variable NumOfProducts shows some outliers, but we can’t be sure of the same for variable Tenure.",2480
Churn_Modelling_boxplots.png,"It is clear that variable EstimatedSalary shows some outliers, but we can’t be sure of the same for variable Tenure.",2481
Churn_Modelling_boxplots.png,"It is clear that variable CreditScore shows some outliers, but we can’t be sure of the same for variable Balance.",2482
Churn_Modelling_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Balance.",2483
Churn_Modelling_histograms_numeric.png,"It is clear that variable Tenure shows some outliers, but we can’t be sure of the same for variable Balance.",2484
Churn_Modelling_boxplots.png,"It is clear that variable NumOfProducts shows some outliers, but we can’t be sure of the same for variable Balance.",2485
Churn_Modelling_histograms_numeric.png,"It is clear that variable EstimatedSalary shows some outliers, but we can’t be sure of the same for variable Balance.",2486
Churn_Modelling_histograms_numeric.png,"It is clear that variable CreditScore shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2487
Churn_Modelling_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2488
Churn_Modelling_histograms_numeric.png,"It is clear that variable Tenure shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2489
Churn_Modelling_histograms_numeric.png,"It is clear that variable Balance shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2490
Churn_Modelling_boxplots.png,"It is clear that variable EstimatedSalary shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2491
Churn_Modelling_histograms_numeric.png,"It is clear that variable CreditScore shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2492
Churn_Modelling_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2493
Churn_Modelling_histograms_numeric.png,"It is clear that variable Tenure shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2494
Churn_Modelling_boxplots.png,"It is clear that variable Balance shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2495
Churn_Modelling_histograms_numeric.png,"It is clear that variable NumOfProducts shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2496
Churn_Modelling_boxplots.png,Those boxplots show that the data is not normalized.,2497
Churn_Modelling_histograms_numeric.png,Variable CreditScore is balanced.,2498
Churn_Modelling_boxplots.png,Variable Age is balanced.,2499
Churn_Modelling_boxplots.png,Variable Tenure is balanced.,2500
Churn_Modelling_histograms_numeric.png,Variable Balance is balanced.,2501
Churn_Modelling_histograms_numeric.png,Variable NumOfProducts is balanced.,2502
Churn_Modelling_boxplots.png,Variable EstimatedSalary is balanced.,2503
Churn_Modelling_histograms.png,The variable CreditScore can be seen as ordinal without losing information.,2504
Churn_Modelling_histograms.png,The variable Geography can be seen as ordinal without losing information.,2505
Churn_Modelling_histograms.png,The variable Gender can be seen as ordinal without losing information.,2506
Churn_Modelling_histograms.png,The variable Age can be seen as ordinal without losing information.,2507
Churn_Modelling_histograms.png,The variable Tenure can be seen as ordinal without losing information.,2508
Churn_Modelling_histograms.png,The variable Balance can be seen as ordinal without losing information.,2509
Churn_Modelling_histograms.png,The variable NumOfProducts can be seen as ordinal without losing information.,2510
Churn_Modelling_histograms.png,The variable HasCrCard can be seen as ordinal without losing information.,2511
Churn_Modelling_histograms.png,The variable IsActiveMember can be seen as ordinal without losing information.,2512
Churn_Modelling_histograms.png,The variable EstimatedSalary can be seen as ordinal without losing information.,2513
Churn_Modelling_histograms.png,The variable CreditScore can be seen as ordinal.,2514
Churn_Modelling_histograms.png,The variable Geography can be seen as ordinal.,2515
Churn_Modelling_histograms.png,The variable Gender can be seen as ordinal.,2516
Churn_Modelling_histograms.png,The variable Age can be seen as ordinal.,2517
Churn_Modelling_histograms.png,The variable Tenure can be seen as ordinal.,2518
Churn_Modelling_histograms.png,The variable Balance can be seen as ordinal.,2519
Churn_Modelling_histograms.png,The variable NumOfProducts can be seen as ordinal.,2520
Churn_Modelling_histograms.png,The variable HasCrCard can be seen as ordinal.,2521
Churn_Modelling_histograms.png,The variable IsActiveMember can be seen as ordinal.,2522
Churn_Modelling_histograms.png,The variable EstimatedSalary can be seen as ordinal.,2523
Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as numeric.",2524
Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as binary.",2525
Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as date.",2526
Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as symbolic.",2527
Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 86.,2528
Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 89.,2529
Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 98.,2530
Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 22.,2531
Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 43.,2532
Churn_Modelling_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,2533
Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",2534
Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",2535
Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",2536
Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",2537
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 0.",2538
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 0.",2539
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 0.",2540
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 0.",2541
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",2542
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",2543
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",2544
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",2545
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 202.",2546
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 202.",2547
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 202.",2548
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 202.",2549
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 202.",2550
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 202.",2551
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 202.",2552
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 202.",2553
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 181.",2554
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 181.",2555
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 181.",2556
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 181.",2557
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 181.",2558
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 181.",2559
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 181.",2560
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 181.",2561
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 137.",2562
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 137.",2563
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 137.",2564
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 137.",2565
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 137.",2566
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 137.",2567
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 137.",2568
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 137.",2569
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 197.",2570
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 197.",2571
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 197.",2572
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 197.",2573
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 197.",2574
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 197.",2575
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 197.",2576
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 197.",2577
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (A,B) as 0.",2578
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (not A, B) as 0.",2579
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (A, not B) as 0.",2580
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (not A, not B) as 0.",2581
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (A,B) as 1.",2582
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (not A, B) as 1.",2583
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (A, not B) as 1.",2584
heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (not A, not B) as 1.",2585
heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 964 episodes.,2586
heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 706 episodes.,2587
heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 932 episodes.,2588
heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1250 episodes.,2589
heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 852 episodes.,2590
heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 83 estimators.,2591
heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 81 estimators.,2592
heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 112 estimators.,2593
heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 130 estimators.,2594
heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 121 estimators.,2595
heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 74 estimators.,2596
heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 52 estimators.,2597
heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 131 estimators.,2598
heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 56 estimators.,2599
heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 139 estimators.,2600
heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,2601
heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,2602
heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,2603
heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,2604
heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,2605
heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,2606
heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,2607
heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,2608
heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,2609
heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,2610
heart_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,2611
heart_decision_tree.png,The recall for the presented tree is higher than its accuracy.,2612
heart_decision_tree.png,The precision for the presented tree is higher than its accuracy.,2613
heart_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,2614
heart_decision_tree.png,The recall for the presented tree is lower than its accuracy.,2615
heart_decision_tree.png,The precision for the presented tree is lower than its accuracy.,2616
heart_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,2617
heart_decision_tree.png,The accuracy for the presented tree is higher than its recall.,2618
heart_decision_tree.png,The precision for the presented tree is higher than its recall.,2619
heart_decision_tree.png,The specificity for the presented tree is higher than its recall.,2620
heart_decision_tree.png,The accuracy for the presented tree is lower than its recall.,2621
heart_decision_tree.png,The precision for the presented tree is lower than its recall.,2622
heart_decision_tree.png,The specificity for the presented tree is lower than its recall.,2623
heart_decision_tree.png,The accuracy for the presented tree is higher than its precision.,2624
heart_decision_tree.png,The recall for the presented tree is higher than its precision.,2625
heart_decision_tree.png,The specificity for the presented tree is higher than its precision.,2626
heart_decision_tree.png,The accuracy for the presented tree is lower than its precision.,2627
heart_decision_tree.png,The recall for the presented tree is lower than its precision.,2628
heart_decision_tree.png,The specificity for the presented tree is lower than its precision.,2629
heart_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,2630
heart_decision_tree.png,The recall for the presented tree is higher than its specificity.,2631
heart_decision_tree.png,The precision for the presented tree is higher than its specificity.,2632
heart_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,2633
heart_decision_tree.png,The recall for the presented tree is lower than its specificity.,2634
heart_decision_tree.png,The precision for the presented tree is lower than its specificity.,2635
heart_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,2636
heart_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,2637
heart_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,2638
heart_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,2639
heart_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,2640
heart_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,2641
heart_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,2642
heart_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,2643
heart_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,2644
heart_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,2645
heart_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,2646
heart_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,2647
heart_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,2648
heart_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,2649
heart_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,2650
heart_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,2651
heart_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,2652
heart_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,2653
heart_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,2654
heart_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,2655
heart_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,2656
heart_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,2657
heart_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,2658
heart_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,2659
heart_decision_tree.png,The number of True Positives reported in the same tree is 13.,2660
heart_decision_tree.png,The number of False Positives reported in the same tree is 38.,2661
heart_decision_tree.png,The number of True Negatives reported in the same tree is 19.,2662
heart_decision_tree.png,The number of False Negatives reported in the same tree is 46.,2663
heart_decision_tree.png,The number of True Positives reported in the same tree is 14.,2664
heart_decision_tree.png,The number of False Positives reported in the same tree is 18.,2665
heart_decision_tree.png,The number of True Negatives reported in the same tree is 42.,2666
heart_decision_tree.png,The number of False Negatives reported in the same tree is 47.,2667
heart_decision_tree.png,The number of True Positives reported in the same tree is 11.,2668
heart_decision_tree.png,The number of False Positives reported in the same tree is 40.,2669
heart_decision_tree.png,The number of True Negatives reported in the same tree is 30.,2670
heart_decision_tree.png,The number of False Negatives reported in the same tree is 35.,2671
heart_decision_tree.png,The number of True Positives reported in the same tree is 29.,2672
heart_decision_tree.png,The number of False Positives reported in the same tree is 50.,2673
heart_decision_tree.png,The number of True Negatives reported in the same tree is 23.,2674
heart_decision_tree.png,The number of False Negatives reported in the same tree is 32.,2675
heart_decision_tree.png,The number of True Positives reported in the same tree is 45.,2676
heart_decision_tree.png,The number of False Positives reported in the same tree is 44.,2677
heart_decision_tree.png,The number of True Negatives reported in the same tree is 26.,2678
heart_decision_tree.png,The number of False Negatives reported in the same tree is 17.,2679
heart_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,2680
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,2681
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,2682
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,2683
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,2684
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,2685
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,2686
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,2687
heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,2688
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,2689
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,2690
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,2691
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,2692
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,2693
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,2694
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,2695
heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,2696
heart_decision_tree.png,The accuracy for the presented tree is higher than 76%.,2697
heart_decision_tree.png,The recall for the presented tree is higher than 74%.,2698
heart_decision_tree.png,The precision for the presented tree is higher than 60%.,2699
heart_decision_tree.png,The specificity for the presented tree is higher than 87%.,2700
heart_decision_tree.png,The accuracy for the presented tree is lower than 75%.,2701
heart_decision_tree.png,The recall for the presented tree is lower than 86%.,2702
heart_decision_tree.png,The precision for the presented tree is lower than 73%.,2703
heart_decision_tree.png,The specificity for the presented tree is lower than 70%.,2704
heart_decision_tree.png,The accuracy for the presented tree is higher than 88%.,2705
heart_decision_tree.png,The recall for the presented tree is higher than 61%.,2706
heart_decision_tree.png,The precision for the presented tree is higher than 83%.,2707
heart_decision_tree.png,The specificity for the presented tree is higher than 64%.,2708
heart_decision_tree.png,The accuracy for the presented tree is lower than 67%.,2709
heart_decision_tree.png,The recall for the presented tree is lower than 82%.,2710
heart_decision_tree.png,The precision for the presented tree is lower than 84%.,2711
heart_decision_tree.png,The specificity for the presented tree is lower than 63%.,2712
heart_decision_tree.png,The accuracy for the presented tree is higher than 81%.,2713
heart_decision_tree.png,The recall for the presented tree is higher than 71%.,2714
heart_decision_tree.png,The precision for the presented tree is higher than 90%.,2715
heart_decision_tree.png,The specificity for the presented tree is higher than 72%.,2716
heart_decision_tree.png,The accuracy for the presented tree is lower than 85%.,2717
heart_decision_tree.png,The recall for the presented tree is lower than 68%.,2718
heart_decision_tree.png,The precision for the presented tree is lower than 89%.,2719
heart_decision_tree.png,The specificity for the presented tree is lower than 69%.,2720
heart_decision_tree.png,The accuracy for the presented tree is higher than 77%.,2721
heart_decision_tree.png,The recall for the presented tree is higher than 66%.,2722
heart_decision_tree.png,The precision for the presented tree is higher than 80%.,2723
heart_decision_tree.png,The specificity for the presented tree is higher than 79%.,2724
heart_decision_tree.png,The accuracy for the presented tree is lower than 78%.,2725
heart_decision_tree.png,The recall for the presented tree is lower than 65%.,2726
heart_decision_tree.png,The precision for the presented tree is lower than 62%.,2727
heart_decision_tree.png,The specificity for the presented tree is lower than 83%.,2728
heart_decision_tree.png,The accuracy for the presented tree is higher than 76%.,2729
heart_decision_tree.png,The recall for the presented tree is higher than 68%.,2730
heart_decision_tree.png,The precision for the presented tree is higher than 86%.,2731
heart_decision_tree.png,The specificity for the presented tree is higher than 77%.,2732
heart_decision_tree.png,The accuracy for the presented tree is lower than 70%.,2733
heart_decision_tree.png,The recall for the presented tree is lower than 90%.,2734
heart_decision_tree.png,The precision for the presented tree is lower than 85%.,2735
heart_decision_tree.png,The specificity for the presented tree is lower than 64%.,2736
heart_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",2737
heart_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",2738
heart_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",2739
heart_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",2740
heart_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",2741
heart_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",2742
heart_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,2743
heart_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,2744
heart_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,2745
heart_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,2746
heart_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,2747
heart_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,2748
heart_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,2749
heart_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,2750
heart_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,2751
heart_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,2752
heart_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,2753
heart_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,2754
heart_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,2755
heart_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,2756
heart_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,2757
heart_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,2758
heart_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,2759
heart_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,2760
heart_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,2761
heart_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,2762
heart_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,2763
heart_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,2764
heart_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,2765
heart_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,2766
heart_overfitting_knn.png,KNN is in overfitting for k less than 2.,2767
heart_overfitting_knn.png,KNN is in overfitting for k larger than 2.,2768
heart_overfitting_knn.png,KNN is in overfitting for k less than 3.,2769
heart_overfitting_knn.png,KNN is in overfitting for k larger than 3.,2770
heart_overfitting_knn.png,KNN is in overfitting for k less than 4.,2771
heart_overfitting_knn.png,KNN is in overfitting for k larger than 4.,2772
heart_overfitting_knn.png,KNN is in overfitting for k less than 5.,2773
heart_overfitting_knn.png,KNN is in overfitting for k larger than 5.,2774
heart_overfitting_knn.png,KNN is in overfitting for k less than 6.,2775
heart_overfitting_knn.png,KNN is in overfitting for k larger than 6.,2776
heart_overfitting_knn.png,KNN is in overfitting for k less than 7.,2777
heart_overfitting_knn.png,KNN is in overfitting for k larger than 7.,2778
heart_overfitting_knn.png,KNN is in overfitting for k less than 8.,2779
heart_overfitting_knn.png,KNN is in overfitting for k larger than 8.,2780
heart_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",2781
heart_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",2782
heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",2783
heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",2784
heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",2785
heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",2786
heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",2787
heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",2788
heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",2789
heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",2790
heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",2791
heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",2792
heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",2793
heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",2794
heart_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,2795
heart_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,2796
heart_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,2797
heart_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,2798
heart_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,2799
heart_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,2800
heart_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,2801
heart_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,2802
heart_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,2803
heart_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,2804
heart_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,2805
heart_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,2806
heart_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,2807
heart_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,2808
heart_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,2809
heart_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,2810
heart_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,2811
heart_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,2812
heart_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,2813
heart_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,2814
heart_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,2815
heart_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,2816
heart_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,2817
heart_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,2818
heart_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,2819
heart_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,2820
heart_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,2821
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable age.,2822
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable age.,2823
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable age.,2824
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable age.,2825
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable age.,2826
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable age.,2827
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable age.,2828
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable age.,2829
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable age.,2830
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable cp.,2831
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable cp.,2832
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable cp.,2833
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable cp.,2834
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable cp.,2835
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable cp.,2836
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable cp.,2837
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable cp.,2838
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable cp.,2839
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable trestbps.,2840
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable trestbps.,2841
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable trestbps.,2842
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable trestbps.,2843
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable trestbps.,2844
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable trestbps.,2845
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable trestbps.,2846
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable trestbps.,2847
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable trestbps.,2848
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable chol.,2849
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable chol.,2850
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable chol.,2851
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable chol.,2852
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable chol.,2853
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable chol.,2854
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable chol.,2855
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable chol.,2856
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable chol.,2857
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable restecg.,2858
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable restecg.,2859
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable restecg.,2860
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable restecg.,2861
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable restecg.,2862
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable restecg.,2863
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable restecg.,2864
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable restecg.,2865
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable restecg.,2866
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable thalach.,2867
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable thalach.,2868
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable thalach.,2869
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable thalach.,2870
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable thalach.,2871
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable thalach.,2872
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable thalach.,2873
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable thalach.,2874
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable thalach.,2875
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable oldpeak.,2876
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable oldpeak.,2877
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable oldpeak.,2878
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable oldpeak.,2879
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable oldpeak.,2880
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable oldpeak.,2881
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable oldpeak.,2882
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable oldpeak.,2883
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable oldpeak.,2884
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable slope.,2885
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable slope.,2886
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable slope.,2887
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable slope.,2888
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable slope.,2889
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable slope.,2890
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable slope.,2891
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable slope.,2892
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable slope.,2893
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable ca.,2894
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable ca.,2895
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable ca.,2896
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable ca.,2897
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable ca.,2898
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable ca.,2899
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable ca.,2900
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable ca.,2901
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable ca.,2902
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable thal.,2903
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable thal.,2904
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable thal.,2905
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable thal.,2906
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable thal.,2907
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable thal.,2908
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable thal.,2909
heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable thal.,2910
heart_pca.png,The first 2 principal components are enough for explaining half the data variance.,2911
heart_pca.png,The first 3 principal components are enough for explaining half the data variance.,2912
heart_pca.png,The first 4 principal components are enough for explaining half the data variance.,2913
heart_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,2914
heart_correlation_heatmap.png,Removing variable age might improve the training of decision trees .,2915
heart_correlation_heatmap.png,Removing variable cp might improve the training of decision trees .,2916
heart_correlation_heatmap.png,Removing variable trestbps might improve the training of decision trees .,2917
heart_correlation_heatmap.png,Removing variable chol might improve the training of decision trees .,2918
heart_correlation_heatmap.png,Removing variable restecg might improve the training of decision trees .,2919
heart_correlation_heatmap.png,Removing variable thalach might improve the training of decision trees .,2920
heart_correlation_heatmap.png,Removing variable oldpeak might improve the training of decision trees .,2921
heart_correlation_heatmap.png,Removing variable slope might improve the training of decision trees .,2922
heart_correlation_heatmap.png,Removing variable ca might improve the training of decision trees .,2923
heart_correlation_heatmap.png,Removing variable thal might improve the training of decision trees .,2924
heart_histograms.png,"Not knowing the semantics of age variable, dummification could have been a more adequate codification.",2925
heart_histograms.png,"Not knowing the semantics of sex variable, dummification could have been a more adequate codification.",2926
heart_histograms.png,"Not knowing the semantics of cp variable, dummification could have been a more adequate codification.",2927
heart_histograms.png,"Not knowing the semantics of trestbps variable, dummification could have been a more adequate codification.",2928
heart_histograms.png,"Not knowing the semantics of chol variable, dummification could have been a more adequate codification.",2929
heart_histograms.png,"Not knowing the semantics of fbs variable, dummification could have been a more adequate codification.",2930
heart_histograms.png,"Not knowing the semantics of restecg variable, dummification could have been a more adequate codification.",2931
heart_histograms.png,"Not knowing the semantics of thalach variable, dummification could have been a more adequate codification.",2932
heart_histograms.png,"Not knowing the semantics of exang variable, dummification could have been a more adequate codification.",2933
heart_histograms.png,"Not knowing the semantics of oldpeak variable, dummification could have been a more adequate codification.",2934
heart_histograms.png,"Not knowing the semantics of slope variable, dummification could have been a more adequate codification.",2935
heart_histograms.png,"Not knowing the semantics of ca variable, dummification could have been a more adequate codification.",2936
heart_histograms.png,"Not knowing the semantics of thal variable, dummification could have been a more adequate codification.",2937
heart_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,2938
heart_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",2939
heart_histograms.png,"Given the usual semantics of age variable, dummification would have been a better codification.",2940
heart_histograms.png,"Given the usual semantics of sex variable, dummification would have been a better codification.",2941
heart_histograms.png,"Given the usual semantics of cp variable, dummification would have been a better codification.",2942
heart_histograms.png,"Given the usual semantics of trestbps variable, dummification would have been a better codification.",2943
heart_histograms.png,"Given the usual semantics of chol variable, dummification would have been a better codification.",2944
heart_histograms.png,"Given the usual semantics of fbs variable, dummification would have been a better codification.",2945
heart_histograms.png,"Given the usual semantics of restecg variable, dummification would have been a better codification.",2946
heart_histograms.png,"Given the usual semantics of thalach variable, dummification would have been a better codification.",2947
heart_histograms.png,"Given the usual semantics of exang variable, dummification would have been a better codification.",2948
heart_histograms.png,"Given the usual semantics of oldpeak variable, dummification would have been a better codification.",2949
heart_histograms.png,"Given the usual semantics of slope variable, dummification would have been a better codification.",2950
heart_histograms.png,"Given the usual semantics of ca variable, dummification would have been a better codification.",2951
heart_histograms.png,"Given the usual semantics of thal variable, dummification would have been a better codification.",2952
heart_histograms.png,The variable age can be coded as ordinal without losing information.,2953
heart_histograms.png,The variable sex can be coded as ordinal without losing information.,2954
heart_histograms.png,The variable cp can be coded as ordinal without losing information.,2955
heart_histograms.png,The variable trestbps can be coded as ordinal without losing information.,2956
heart_histograms.png,The variable chol can be coded as ordinal without losing information.,2957
heart_histograms.png,The variable fbs can be coded as ordinal without losing information.,2958
heart_histograms.png,The variable restecg can be coded as ordinal without losing information.,2959
heart_histograms.png,The variable thalach can be coded as ordinal without losing information.,2960
heart_histograms.png,The variable exang can be coded as ordinal without losing information.,2961
heart_histograms.png,The variable oldpeak can be coded as ordinal without losing information.,2962
heart_histograms.png,The variable slope can be coded as ordinal without losing information.,2963
heart_histograms.png,The variable ca can be coded as ordinal without losing information.,2964
heart_histograms.png,The variable thal can be coded as ordinal without losing information.,2965
heart_histograms.png,"Considering the common semantics for age variable, dummification would be the most adequate encoding.",2966
heart_histograms.png,"Considering the common semantics for sex variable, dummification would be the most adequate encoding.",2967
heart_histograms.png,"Considering the common semantics for cp variable, dummification would be the most adequate encoding.",2968
heart_histograms.png,"Considering the common semantics for trestbps variable, dummification would be the most adequate encoding.",2969
heart_histograms.png,"Considering the common semantics for chol variable, dummification would be the most adequate encoding.",2970
heart_histograms.png,"Considering the common semantics for fbs variable, dummification would be the most adequate encoding.",2971
heart_histograms.png,"Considering the common semantics for restecg variable, dummification would be the most adequate encoding.",2972
heart_histograms.png,"Considering the common semantics for thalach variable, dummification would be the most adequate encoding.",2973
heart_histograms.png,"Considering the common semantics for exang variable, dummification would be the most adequate encoding.",2974
heart_histograms.png,"Considering the common semantics for oldpeak variable, dummification would be the most adequate encoding.",2975
heart_histograms.png,"Considering the common semantics for slope variable, dummification would be the most adequate encoding.",2976
heart_histograms.png,"Considering the common semantics for ca variable, dummification would be the most adequate encoding.",2977
heart_histograms.png,"Considering the common semantics for thal variable, dummification would be the most adequate encoding.",2978
heart_histograms.png,"Considering the common semantics for sex and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2979
heart_histograms.png,"Considering the common semantics for cp and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2980
heart_histograms.png,"Considering the common semantics for trestbps and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2981
heart_histograms.png,"Considering the common semantics for chol and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2982
heart_histograms.png,"Considering the common semantics for fbs and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2983
heart_histograms.png,"Considering the common semantics for restecg and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2984
heart_histograms.png,"Considering the common semantics for thalach and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2985
heart_histograms.png,"Considering the common semantics for exang and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2986
heart_histograms.png,"Considering the common semantics for oldpeak and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2987
heart_histograms.png,"Considering the common semantics for slope and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2988
heart_histograms.png,"Considering the common semantics for ca and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2989
heart_histograms.png,"Considering the common semantics for thal and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2990
heart_histograms.png,"Considering the common semantics for age and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2991
heart_histograms.png,"Considering the common semantics for cp and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2992
heart_histograms.png,"Considering the common semantics for trestbps and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2993
heart_histograms.png,"Considering the common semantics for chol and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2994
heart_histograms.png,"Considering the common semantics for fbs and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2995
heart_histograms.png,"Considering the common semantics for restecg and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2996
heart_histograms.png,"Considering the common semantics for thalach and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2997
heart_histograms.png,"Considering the common semantics for exang and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2998
heart_histograms.png,"Considering the common semantics for oldpeak and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2999
heart_histograms.png,"Considering the common semantics for slope and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3000
heart_histograms.png,"Considering the common semantics for ca and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3001
heart_histograms.png,"Considering the common semantics for thal and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3002
heart_histograms.png,"Considering the common semantics for age and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3003
heart_histograms.png,"Considering the common semantics for sex and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3004
heart_histograms.png,"Considering the common semantics for trestbps and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3005
heart_histograms.png,"Considering the common semantics for chol and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3006
heart_histograms.png,"Considering the common semantics for fbs and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3007
heart_histograms.png,"Considering the common semantics for restecg and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3008
heart_histograms.png,"Considering the common semantics for thalach and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3009
heart_histograms.png,"Considering the common semantics for exang and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3010
heart_histograms.png,"Considering the common semantics for oldpeak and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3011
heart_histograms.png,"Considering the common semantics for slope and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3012
heart_histograms.png,"Considering the common semantics for ca and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3013
heart_histograms.png,"Considering the common semantics for thal and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3014
heart_histograms.png,"Considering the common semantics for age and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3015
heart_histograms.png,"Considering the common semantics for sex and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3016
heart_histograms.png,"Considering the common semantics for cp and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3017
heart_histograms.png,"Considering the common semantics for chol and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3018
heart_histograms.png,"Considering the common semantics for fbs and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3019
heart_histograms.png,"Considering the common semantics for restecg and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3020
heart_histograms.png,"Considering the common semantics for thalach and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3021
heart_histograms.png,"Considering the common semantics for exang and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3022
heart_histograms.png,"Considering the common semantics for oldpeak and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3023
heart_histograms.png,"Considering the common semantics for slope and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3024
heart_histograms.png,"Considering the common semantics for ca and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3025
heart_histograms.png,"Considering the common semantics for thal and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3026
heart_histograms.png,"Considering the common semantics for age and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3027
heart_histograms.png,"Considering the common semantics for sex and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3028
heart_histograms.png,"Considering the common semantics for cp and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3029
heart_histograms.png,"Considering the common semantics for trestbps and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3030
heart_histograms.png,"Considering the common semantics for fbs and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3031
heart_histograms.png,"Considering the common semantics for restecg and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3032
heart_histograms.png,"Considering the common semantics for thalach and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3033
heart_histograms.png,"Considering the common semantics for exang and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3034
heart_histograms.png,"Considering the common semantics for oldpeak and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3035
heart_histograms.png,"Considering the common semantics for slope and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3036
heart_histograms.png,"Considering the common semantics for ca and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3037
heart_histograms.png,"Considering the common semantics for thal and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3038
heart_histograms.png,"Considering the common semantics for age and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3039
heart_histograms.png,"Considering the common semantics for sex and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3040
heart_histograms.png,"Considering the common semantics for cp and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3041
heart_histograms.png,"Considering the common semantics for trestbps and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3042
heart_histograms.png,"Considering the common semantics for chol and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3043
heart_histograms.png,"Considering the common semantics for restecg and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3044
heart_histograms.png,"Considering the common semantics for thalach and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3045
heart_histograms.png,"Considering the common semantics for exang and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3046
heart_histograms.png,"Considering the common semantics for oldpeak and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3047
heart_histograms.png,"Considering the common semantics for slope and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3048
heart_histograms.png,"Considering the common semantics for ca and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3049
heart_histograms.png,"Considering the common semantics for thal and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3050
heart_histograms.png,"Considering the common semantics for age and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3051
heart_histograms.png,"Considering the common semantics for sex and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3052
heart_histograms.png,"Considering the common semantics for cp and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3053
heart_histograms.png,"Considering the common semantics for trestbps and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3054
heart_histograms.png,"Considering the common semantics for chol and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3055
heart_histograms.png,"Considering the common semantics for fbs and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3056
heart_histograms.png,"Considering the common semantics for thalach and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3057
heart_histograms.png,"Considering the common semantics for exang and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3058
heart_histograms.png,"Considering the common semantics for oldpeak and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3059
heart_histograms.png,"Considering the common semantics for slope and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3060
heart_histograms.png,"Considering the common semantics for ca and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3061
heart_histograms.png,"Considering the common semantics for thal and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3062
heart_histograms.png,"Considering the common semantics for age and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3063
heart_histograms.png,"Considering the common semantics for sex and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3064
heart_histograms.png,"Considering the common semantics for cp and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3065
heart_histograms.png,"Considering the common semantics for trestbps and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3066
heart_histograms.png,"Considering the common semantics for chol and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3067
heart_histograms.png,"Considering the common semantics for fbs and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3068
heart_histograms.png,"Considering the common semantics for restecg and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3069
heart_histograms.png,"Considering the common semantics for exang and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3070
heart_histograms.png,"Considering the common semantics for oldpeak and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3071
heart_histograms.png,"Considering the common semantics for slope and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3072
heart_histograms.png,"Considering the common semantics for ca and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3073
heart_histograms.png,"Considering the common semantics for thal and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3074
heart_histograms.png,"Considering the common semantics for age and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3075
heart_histograms.png,"Considering the common semantics for sex and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3076
heart_histograms.png,"Considering the common semantics for cp and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3077
heart_histograms.png,"Considering the common semantics for trestbps and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3078
heart_histograms.png,"Considering the common semantics for chol and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3079
heart_histograms.png,"Considering the common semantics for fbs and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3080
heart_histograms.png,"Considering the common semantics for restecg and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3081
heart_histograms.png,"Considering the common semantics for thalach and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3082
heart_histograms.png,"Considering the common semantics for oldpeak and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3083
heart_histograms.png,"Considering the common semantics for slope and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3084
heart_histograms.png,"Considering the common semantics for ca and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3085
heart_histograms.png,"Considering the common semantics for thal and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3086
heart_histograms.png,"Considering the common semantics for age and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3087
heart_histograms.png,"Considering the common semantics for sex and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3088
heart_histograms.png,"Considering the common semantics for cp and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3089
heart_histograms.png,"Considering the common semantics for trestbps and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3090
heart_histograms.png,"Considering the common semantics for chol and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3091
heart_histograms.png,"Considering the common semantics for fbs and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3092
heart_histograms.png,"Considering the common semantics for restecg and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3093
heart_histograms.png,"Considering the common semantics for thalach and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3094
heart_histograms.png,"Considering the common semantics for exang and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3095
heart_histograms.png,"Considering the common semantics for slope and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3096
heart_histograms.png,"Considering the common semantics for ca and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3097
heart_histograms.png,"Considering the common semantics for thal and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3098
heart_histograms.png,"Considering the common semantics for age and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3099
heart_histograms.png,"Considering the common semantics for sex and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3100
heart_histograms.png,"Considering the common semantics for cp and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3101
heart_histograms.png,"Considering the common semantics for trestbps and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3102
heart_histograms.png,"Considering the common semantics for chol and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3103
heart_histograms.png,"Considering the common semantics for fbs and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3104
heart_histograms.png,"Considering the common semantics for restecg and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3105
heart_histograms.png,"Considering the common semantics for thalach and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3106
heart_histograms.png,"Considering the common semantics for exang and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3107
heart_histograms.png,"Considering the common semantics for oldpeak and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3108
heart_histograms.png,"Considering the common semantics for ca and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3109
heart_histograms.png,"Considering the common semantics for thal and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3110
heart_histograms.png,"Considering the common semantics for age and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3111
heart_histograms.png,"Considering the common semantics for sex and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3112
heart_histograms.png,"Considering the common semantics for cp and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3113
heart_histograms.png,"Considering the common semantics for trestbps and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3114
heart_histograms.png,"Considering the common semantics for chol and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3115
heart_histograms.png,"Considering the common semantics for fbs and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3116
heart_histograms.png,"Considering the common semantics for restecg and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3117
heart_histograms.png,"Considering the common semantics for exang and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3118
heart_histograms.png,"Considering the common semantics for oldpeak and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3119
heart_histograms.png,"Considering the common semantics for slope and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3120
heart_histograms.png,"Considering the common semantics for ca and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3121
heart_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,3122
heart_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,3123
heart_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,3124
heart_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",3125
heart_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",3126
heart_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",3127
heart_correlation_heatmap.png,Variables cp and age seem to be useful for classification tasks.,3128
heart_correlation_heatmap.png,Variables trestbps and age seem to be useful for classification tasks.,3129
heart_correlation_heatmap.png,Variables chol and age seem to be useful for classification tasks.,3130
heart_correlation_heatmap.png,Variables restecg and age seem to be useful for classification tasks.,3131
heart_correlation_heatmap.png,Variables thalach and age seem to be useful for classification tasks.,3132
heart_correlation_heatmap.png,Variables oldpeak and age seem to be useful for classification tasks.,3133
heart_correlation_heatmap.png,Variables slope and age seem to be useful for classification tasks.,3134
heart_correlation_heatmap.png,Variables ca and age seem to be useful for classification tasks.,3135
heart_correlation_heatmap.png,Variables thal and age seem to be useful for classification tasks.,3136
heart_correlation_heatmap.png,Variables age and cp seem to be useful for classification tasks.,3137
heart_correlation_heatmap.png,Variables trestbps and cp seem to be useful for classification tasks.,3138
heart_correlation_heatmap.png,Variables chol and cp seem to be useful for classification tasks.,3139
heart_correlation_heatmap.png,Variables restecg and cp seem to be useful for classification tasks.,3140
heart_correlation_heatmap.png,Variables thalach and cp seem to be useful for classification tasks.,3141
heart_correlation_heatmap.png,Variables oldpeak and cp seem to be useful for classification tasks.,3142
heart_correlation_heatmap.png,Variables slope and cp seem to be useful for classification tasks.,3143
heart_correlation_heatmap.png,Variables ca and cp seem to be useful for classification tasks.,3144
heart_correlation_heatmap.png,Variables thal and cp seem to be useful for classification tasks.,3145
heart_correlation_heatmap.png,Variables age and trestbps seem to be useful for classification tasks.,3146
heart_correlation_heatmap.png,Variables cp and trestbps seem to be useful for classification tasks.,3147
heart_correlation_heatmap.png,Variables chol and trestbps seem to be useful for classification tasks.,3148
heart_correlation_heatmap.png,Variables restecg and trestbps seem to be useful for classification tasks.,3149
heart_correlation_heatmap.png,Variables thalach and trestbps seem to be useful for classification tasks.,3150
heart_correlation_heatmap.png,Variables oldpeak and trestbps seem to be useful for classification tasks.,3151
heart_correlation_heatmap.png,Variables slope and trestbps seem to be useful for classification tasks.,3152
heart_correlation_heatmap.png,Variables ca and trestbps seem to be useful for classification tasks.,3153
heart_correlation_heatmap.png,Variables thal and trestbps seem to be useful for classification tasks.,3154
heart_correlation_heatmap.png,Variables age and chol seem to be useful for classification tasks.,3155
heart_correlation_heatmap.png,Variables cp and chol seem to be useful for classification tasks.,3156
heart_correlation_heatmap.png,Variables trestbps and chol seem to be useful for classification tasks.,3157
heart_correlation_heatmap.png,Variables restecg and chol seem to be useful for classification tasks.,3158
heart_correlation_heatmap.png,Variables thalach and chol seem to be useful for classification tasks.,3159
heart_correlation_heatmap.png,Variables oldpeak and chol seem to be useful for classification tasks.,3160
heart_correlation_heatmap.png,Variables slope and chol seem to be useful for classification tasks.,3161
heart_correlation_heatmap.png,Variables ca and chol seem to be useful for classification tasks.,3162
heart_correlation_heatmap.png,Variables thal and chol seem to be useful for classification tasks.,3163
heart_correlation_heatmap.png,Variables age and restecg seem to be useful for classification tasks.,3164
heart_correlation_heatmap.png,Variables cp and restecg seem to be useful for classification tasks.,3165
heart_correlation_heatmap.png,Variables trestbps and restecg seem to be useful for classification tasks.,3166
heart_correlation_heatmap.png,Variables chol and restecg seem to be useful for classification tasks.,3167
heart_correlation_heatmap.png,Variables thalach and restecg seem to be useful for classification tasks.,3168
heart_correlation_heatmap.png,Variables oldpeak and restecg seem to be useful for classification tasks.,3169
heart_correlation_heatmap.png,Variables slope and restecg seem to be useful for classification tasks.,3170
heart_correlation_heatmap.png,Variables ca and restecg seem to be useful for classification tasks.,3171
heart_correlation_heatmap.png,Variables thal and restecg seem to be useful for classification tasks.,3172
heart_correlation_heatmap.png,Variables age and thalach seem to be useful for classification tasks.,3173
heart_correlation_heatmap.png,Variables cp and thalach seem to be useful for classification tasks.,3174
heart_correlation_heatmap.png,Variables trestbps and thalach seem to be useful for classification tasks.,3175
heart_correlation_heatmap.png,Variables chol and thalach seem to be useful for classification tasks.,3176
heart_correlation_heatmap.png,Variables restecg and thalach seem to be useful for classification tasks.,3177
heart_correlation_heatmap.png,Variables oldpeak and thalach seem to be useful for classification tasks.,3178
heart_correlation_heatmap.png,Variables slope and thalach seem to be useful for classification tasks.,3179
heart_correlation_heatmap.png,Variables ca and thalach seem to be useful for classification tasks.,3180
heart_correlation_heatmap.png,Variables thal and thalach seem to be useful for classification tasks.,3181
heart_correlation_heatmap.png,Variables age and oldpeak seem to be useful for classification tasks.,3182
heart_correlation_heatmap.png,Variables cp and oldpeak seem to be useful for classification tasks.,3183
heart_correlation_heatmap.png,Variables trestbps and oldpeak seem to be useful for classification tasks.,3184
heart_correlation_heatmap.png,Variables chol and oldpeak seem to be useful for classification tasks.,3185
heart_correlation_heatmap.png,Variables restecg and oldpeak seem to be useful for classification tasks.,3186
heart_correlation_heatmap.png,Variables thalach and oldpeak seem to be useful for classification tasks.,3187
heart_correlation_heatmap.png,Variables slope and oldpeak seem to be useful for classification tasks.,3188
heart_correlation_heatmap.png,Variables ca and oldpeak seem to be useful for classification tasks.,3189
heart_correlation_heatmap.png,Variables thal and oldpeak seem to be useful for classification tasks.,3190
heart_correlation_heatmap.png,Variables age and slope seem to be useful for classification tasks.,3191
heart_correlation_heatmap.png,Variables cp and slope seem to be useful for classification tasks.,3192
heart_correlation_heatmap.png,Variables trestbps and slope seem to be useful for classification tasks.,3193
heart_correlation_heatmap.png,Variables chol and slope seem to be useful for classification tasks.,3194
heart_correlation_heatmap.png,Variables restecg and slope seem to be useful for classification tasks.,3195
heart_correlation_heatmap.png,Variables thalach and slope seem to be useful for classification tasks.,3196
heart_correlation_heatmap.png,Variables oldpeak and slope seem to be useful for classification tasks.,3197
heart_correlation_heatmap.png,Variables ca and slope seem to be useful for classification tasks.,3198
heart_correlation_heatmap.png,Variables thal and slope seem to be useful for classification tasks.,3199
heart_correlation_heatmap.png,Variables age and thal seem to be useful for classification tasks.,3200
heart_correlation_heatmap.png,Variables cp and thal seem to be useful for classification tasks.,3201
heart_correlation_heatmap.png,Variables trestbps and thal seem to be useful for classification tasks.,3202
heart_correlation_heatmap.png,Variables chol and thal seem to be useful for classification tasks.,3203
heart_correlation_heatmap.png,Variables restecg and thal seem to be useful for classification tasks.,3204
heart_correlation_heatmap.png,Variables oldpeak and thal seem to be useful for classification tasks.,3205
heart_correlation_heatmap.png,Variables slope and thal seem to be useful for classification tasks.,3206
heart_correlation_heatmap.png,Variables ca and thal seem to be useful for classification tasks.,3207
heart_correlation_heatmap.png,Variable age seems to be relevant for the majority of mining tasks.,3208
heart_correlation_heatmap.png,Variable cp seems to be relevant for the majority of mining tasks.,3209
heart_correlation_heatmap.png,Variable trestbps seems to be relevant for the majority of mining tasks.,3210
heart_correlation_heatmap.png,Variable chol seems to be relevant for the majority of mining tasks.,3211
heart_correlation_heatmap.png,Variable restecg seems to be relevant for the majority of mining tasks.,3212
heart_correlation_heatmap.png,Variable thalach seems to be relevant for the majority of mining tasks.,3213
heart_correlation_heatmap.png,Variable oldpeak seems to be relevant for the majority of mining tasks.,3214
heart_correlation_heatmap.png,Variable slope seems to be relevant for the majority of mining tasks.,3215
heart_correlation_heatmap.png,Variable ca seems to be relevant for the majority of mining tasks.,3216
heart_correlation_heatmap.png,Variable thal seems to be relevant for the majority of mining tasks.,3217
heart_decision_tree.png,Variable age is one of the most relevant variables.,3218
heart_decision_tree.png,Variable cp is one of the most relevant variables.,3219
heart_decision_tree.png,Variable trestbps is one of the most relevant variables.,3220
heart_decision_tree.png,Variable chol is one of the most relevant variables.,3221
heart_decision_tree.png,Variable restecg is one of the most relevant variables.,3222
heart_decision_tree.png,Variable thalach is one of the most relevant variables.,3223
heart_decision_tree.png,Variable oldpeak is one of the most relevant variables.,3224
heart_decision_tree.png,Variable slope is one of the most relevant variables.,3225
heart_decision_tree.png,Variable ca is one of the most relevant variables.,3226
heart_decision_tree.png,Variable thal is one of the most relevant variables.,3227
heart_decision_tree.png,It is possible to state that age is the first most discriminative variable regarding the class.,3228
heart_decision_tree.png,It is possible to state that cp is the first most discriminative variable regarding the class.,3229
heart_decision_tree.png,It is possible to state that trestbps is the first most discriminative variable regarding the class.,3230
heart_decision_tree.png,It is possible to state that chol is the first most discriminative variable regarding the class.,3231
heart_decision_tree.png,It is possible to state that restecg is the first most discriminative variable regarding the class.,3232
heart_decision_tree.png,It is possible to state that thalach is the first most discriminative variable regarding the class.,3233
heart_decision_tree.png,It is possible to state that oldpeak is the first most discriminative variable regarding the class.,3234
heart_decision_tree.png,It is possible to state that slope is the first most discriminative variable regarding the class.,3235
heart_decision_tree.png,It is possible to state that ca is the first most discriminative variable regarding the class.,3236
heart_decision_tree.png,It is possible to state that thal is the first most discriminative variable regarding the class.,3237
heart_decision_tree.png,It is possible to state that age is the second most discriminative variable regarding the class.,3238
heart_decision_tree.png,It is possible to state that cp is the second most discriminative variable regarding the class.,3239
heart_decision_tree.png,It is possible to state that trestbps is the second most discriminative variable regarding the class.,3240
heart_decision_tree.png,It is possible to state that chol is the second most discriminative variable regarding the class.,3241
heart_decision_tree.png,It is possible to state that restecg is the second most discriminative variable regarding the class.,3242
heart_decision_tree.png,It is possible to state that thalach is the second most discriminative variable regarding the class.,3243
heart_decision_tree.png,It is possible to state that oldpeak is the second most discriminative variable regarding the class.,3244
heart_decision_tree.png,It is possible to state that slope is the second most discriminative variable regarding the class.,3245
heart_decision_tree.png,It is possible to state that ca is the second most discriminative variable regarding the class.,3246
heart_decision_tree.png,It is possible to state that thal is the second most discriminative variable regarding the class.,3247
heart_decision_tree.png,"The variable age discriminates between the target values, as shown in the decision tree.",3248
heart_decision_tree.png,"The variable cp discriminates between the target values, as shown in the decision tree.",3249
heart_decision_tree.png,"The variable trestbps discriminates between the target values, as shown in the decision tree.",3250
heart_decision_tree.png,"The variable chol discriminates between the target values, as shown in the decision tree.",3251
heart_decision_tree.png,"The variable restecg discriminates between the target values, as shown in the decision tree.",3252
heart_decision_tree.png,"The variable thalach discriminates between the target values, as shown in the decision tree.",3253
heart_decision_tree.png,"The variable oldpeak discriminates between the target values, as shown in the decision tree.",3254
heart_decision_tree.png,"The variable slope discriminates between the target values, as shown in the decision tree.",3255
heart_decision_tree.png,"The variable ca discriminates between the target values, as shown in the decision tree.",3256
heart_decision_tree.png,"The variable thal discriminates between the target values, as shown in the decision tree.",3257
heart_decision_tree.png,The variable age seems to be one of the two most relevant features.,3258
heart_decision_tree.png,The variable cp seems to be one of the two most relevant features.,3259
heart_decision_tree.png,The variable trestbps seems to be one of the two most relevant features.,3260
heart_decision_tree.png,The variable chol seems to be one of the two most relevant features.,3261
heart_decision_tree.png,The variable restecg seems to be one of the two most relevant features.,3262
heart_decision_tree.png,The variable thalach seems to be one of the two most relevant features.,3263
heart_decision_tree.png,The variable oldpeak seems to be one of the two most relevant features.,3264
heart_decision_tree.png,The variable slope seems to be one of the two most relevant features.,3265
heart_decision_tree.png,The variable ca seems to be one of the two most relevant features.,3266
heart_decision_tree.png,The variable thal seems to be one of the two most relevant features.,3267
heart_decision_tree.png,The variable age seems to be one of the three most relevant features.,3268
heart_decision_tree.png,The variable cp seems to be one of the three most relevant features.,3269
heart_decision_tree.png,The variable trestbps seems to be one of the three most relevant features.,3270
heart_decision_tree.png,The variable chol seems to be one of the three most relevant features.,3271
heart_decision_tree.png,The variable restecg seems to be one of the three most relevant features.,3272
heart_decision_tree.png,The variable thalach seems to be one of the three most relevant features.,3273
heart_decision_tree.png,The variable oldpeak seems to be one of the three most relevant features.,3274
heart_decision_tree.png,The variable slope seems to be one of the three most relevant features.,3275
heart_decision_tree.png,The variable ca seems to be one of the three most relevant features.,3276
heart_decision_tree.png,The variable thal seems to be one of the three most relevant features.,3277
heart_decision_tree.png,The variable age seems to be one of the four most relevant features.,3278
heart_decision_tree.png,The variable cp seems to be one of the four most relevant features.,3279
heart_decision_tree.png,The variable trestbps seems to be one of the four most relevant features.,3280
heart_decision_tree.png,The variable chol seems to be one of the four most relevant features.,3281
heart_decision_tree.png,The variable restecg seems to be one of the four most relevant features.,3282
heart_decision_tree.png,The variable thalach seems to be one of the four most relevant features.,3283
heart_decision_tree.png,The variable oldpeak seems to be one of the four most relevant features.,3284
heart_decision_tree.png,The variable slope seems to be one of the four most relevant features.,3285
heart_decision_tree.png,The variable ca seems to be one of the four most relevant features.,3286
heart_decision_tree.png,The variable thal seems to be one of the four most relevant features.,3287
heart_decision_tree.png,The variable age seems to be one of the five most relevant features.,3288
heart_decision_tree.png,The variable cp seems to be one of the five most relevant features.,3289
heart_decision_tree.png,The variable trestbps seems to be one of the five most relevant features.,3290
heart_decision_tree.png,The variable chol seems to be one of the five most relevant features.,3291
heart_decision_tree.png,The variable restecg seems to be one of the five most relevant features.,3292
heart_decision_tree.png,The variable thalach seems to be one of the five most relevant features.,3293
heart_decision_tree.png,The variable oldpeak seems to be one of the five most relevant features.,3294
heart_decision_tree.png,The variable slope seems to be one of the five most relevant features.,3295
heart_decision_tree.png,The variable ca seems to be one of the five most relevant features.,3296
heart_decision_tree.png,The variable thal seems to be one of the five most relevant features.,3297
heart_decision_tree.png,It is clear that variable age is one of the two most relevant features.,3298
heart_decision_tree.png,It is clear that variable cp is one of the two most relevant features.,3299
heart_decision_tree.png,It is clear that variable trestbps is one of the two most relevant features.,3300
heart_decision_tree.png,It is clear that variable chol is one of the two most relevant features.,3301
heart_decision_tree.png,It is clear that variable restecg is one of the two most relevant features.,3302
heart_decision_tree.png,It is clear that variable thalach is one of the two most relevant features.,3303
heart_decision_tree.png,It is clear that variable oldpeak is one of the two most relevant features.,3304
heart_decision_tree.png,It is clear that variable slope is one of the two most relevant features.,3305
heart_decision_tree.png,It is clear that variable ca is one of the two most relevant features.,3306
heart_decision_tree.png,It is clear that variable thal is one of the two most relevant features.,3307
heart_decision_tree.png,It is clear that variable age is one of the three most relevant features.,3308
heart_decision_tree.png,It is clear that variable cp is one of the three most relevant features.,3309
heart_decision_tree.png,It is clear that variable trestbps is one of the three most relevant features.,3310
heart_decision_tree.png,It is clear that variable chol is one of the three most relevant features.,3311
heart_decision_tree.png,It is clear that variable restecg is one of the three most relevant features.,3312
heart_decision_tree.png,It is clear that variable thalach is one of the three most relevant features.,3313
heart_decision_tree.png,It is clear that variable oldpeak is one of the three most relevant features.,3314
heart_decision_tree.png,It is clear that variable slope is one of the three most relevant features.,3315
heart_decision_tree.png,It is clear that variable ca is one of the three most relevant features.,3316
heart_decision_tree.png,It is clear that variable thal is one of the three most relevant features.,3317
heart_decision_tree.png,It is clear that variable age is one of the four most relevant features.,3318
heart_decision_tree.png,It is clear that variable cp is one of the four most relevant features.,3319
heart_decision_tree.png,It is clear that variable trestbps is one of the four most relevant features.,3320
heart_decision_tree.png,It is clear that variable chol is one of the four most relevant features.,3321
heart_decision_tree.png,It is clear that variable restecg is one of the four most relevant features.,3322
heart_decision_tree.png,It is clear that variable thalach is one of the four most relevant features.,3323
heart_decision_tree.png,It is clear that variable oldpeak is one of the four most relevant features.,3324
heart_decision_tree.png,It is clear that variable slope is one of the four most relevant features.,3325
heart_decision_tree.png,It is clear that variable ca is one of the four most relevant features.,3326
heart_decision_tree.png,It is clear that variable thal is one of the four most relevant features.,3327
heart_decision_tree.png,It is clear that variable age is one of the five most relevant features.,3328
heart_decision_tree.png,It is clear that variable cp is one of the five most relevant features.,3329
heart_decision_tree.png,It is clear that variable trestbps is one of the five most relevant features.,3330
heart_decision_tree.png,It is clear that variable chol is one of the five most relevant features.,3331
heart_decision_tree.png,It is clear that variable restecg is one of the five most relevant features.,3332
heart_decision_tree.png,It is clear that variable thalach is one of the five most relevant features.,3333
heart_decision_tree.png,It is clear that variable oldpeak is one of the five most relevant features.,3334
heart_decision_tree.png,It is clear that variable slope is one of the five most relevant features.,3335
heart_decision_tree.png,It is clear that variable ca is one of the five most relevant features.,3336
heart_decision_tree.png,It is clear that variable thal is one of the five most relevant features.,3337
heart_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",3338
heart_correlation_heatmap.png,Variables cp and age are redundant.,3339
heart_correlation_heatmap.png,Variables trestbps and age are redundant.,3340
heart_correlation_heatmap.png,Variables chol and age are redundant.,3341
heart_correlation_heatmap.png,Variables restecg and age are redundant.,3342
heart_correlation_heatmap.png,Variables thalach and age are redundant.,3343
heart_correlation_heatmap.png,Variables oldpeak and age are redundant.,3344
heart_correlation_heatmap.png,Variables slope and age are redundant.,3345
heart_correlation_heatmap.png,Variables ca and age are redundant.,3346
heart_correlation_heatmap.png,Variables thal and age are redundant.,3347
heart_correlation_heatmap.png,Variables age and cp are redundant.,3348
heart_correlation_heatmap.png,Variables trestbps and cp are redundant.,3349
heart_correlation_heatmap.png,Variables chol and cp are redundant.,3350
heart_correlation_heatmap.png,Variables restecg and cp are redundant.,3351
heart_correlation_heatmap.png,Variables thalach and cp are redundant.,3352
heart_correlation_heatmap.png,Variables oldpeak and cp are redundant.,3353
heart_correlation_heatmap.png,Variables slope and cp are redundant.,3354
heart_correlation_heatmap.png,Variables ca and cp are redundant.,3355
heart_correlation_heatmap.png,Variables thal and cp are redundant.,3356
heart_correlation_heatmap.png,Variables age and trestbps are redundant.,3357
heart_correlation_heatmap.png,Variables cp and trestbps are redundant.,3358
heart_correlation_heatmap.png,Variables chol and trestbps are redundant.,3359
heart_correlation_heatmap.png,Variables restecg and trestbps are redundant.,3360
heart_correlation_heatmap.png,Variables thalach and trestbps are redundant.,3361
heart_correlation_heatmap.png,Variables oldpeak and trestbps are redundant.,3362
heart_correlation_heatmap.png,Variables slope and trestbps are redundant.,3363
heart_correlation_heatmap.png,Variables ca and trestbps are redundant.,3364
heart_correlation_heatmap.png,Variables thal and trestbps are redundant.,3365
heart_correlation_heatmap.png,Variables age and chol are redundant.,3366
heart_correlation_heatmap.png,Variables cp and chol are redundant.,3367
heart_correlation_heatmap.png,Variables trestbps and chol are redundant.,3368
heart_correlation_heatmap.png,Variables restecg and chol are redundant.,3369
heart_correlation_heatmap.png,Variables thalach and chol are redundant.,3370
heart_correlation_heatmap.png,Variables oldpeak and chol are redundant.,3371
heart_correlation_heatmap.png,Variables slope and chol are redundant.,3372
heart_correlation_heatmap.png,Variables ca and chol are redundant.,3373
heart_correlation_heatmap.png,Variables thal and chol are redundant.,3374
heart_correlation_heatmap.png,Variables age and restecg are redundant.,3375
heart_correlation_heatmap.png,Variables cp and restecg are redundant.,3376
heart_correlation_heatmap.png,Variables trestbps and restecg are redundant.,3377
heart_correlation_heatmap.png,Variables chol and restecg are redundant.,3378
heart_correlation_heatmap.png,Variables thalach and restecg are redundant.,3379
heart_correlation_heatmap.png,Variables oldpeak and restecg are redundant.,3380
heart_correlation_heatmap.png,Variables slope and restecg are redundant.,3381
heart_correlation_heatmap.png,Variables ca and restecg are redundant.,3382
heart_correlation_heatmap.png,Variables thal and restecg are redundant.,3383
heart_correlation_heatmap.png,Variables age and thalach are redundant.,3384
heart_correlation_heatmap.png,Variables cp and thalach are redundant.,3385
heart_correlation_heatmap.png,Variables trestbps and thalach are redundant.,3386
heart_correlation_heatmap.png,Variables chol and thalach are redundant.,3387
heart_correlation_heatmap.png,Variables restecg and thalach are redundant.,3388
heart_correlation_heatmap.png,Variables oldpeak and thalach are redundant.,3389
heart_correlation_heatmap.png,Variables slope and thalach are redundant.,3390
heart_correlation_heatmap.png,Variables ca and thalach are redundant.,3391
heart_correlation_heatmap.png,Variables thal and thalach are redundant.,3392
heart_correlation_heatmap.png,Variables age and oldpeak are redundant.,3393
heart_correlation_heatmap.png,Variables cp and oldpeak are redundant.,3394
heart_correlation_heatmap.png,Variables trestbps and oldpeak are redundant.,3395
heart_correlation_heatmap.png,Variables chol and oldpeak are redundant.,3396
heart_correlation_heatmap.png,Variables restecg and oldpeak are redundant.,3397
heart_correlation_heatmap.png,Variables thalach and oldpeak are redundant.,3398
heart_correlation_heatmap.png,Variables slope and oldpeak are redundant.,3399
heart_correlation_heatmap.png,Variables ca and oldpeak are redundant.,3400
heart_correlation_heatmap.png,Variables thal and oldpeak are redundant.,3401
heart_correlation_heatmap.png,Variables age and slope are redundant.,3402
heart_correlation_heatmap.png,Variables cp and slope are redundant.,3403
heart_correlation_heatmap.png,Variables trestbps and slope are redundant.,3404
heart_correlation_heatmap.png,Variables chol and slope are redundant.,3405
heart_correlation_heatmap.png,Variables restecg and slope are redundant.,3406
heart_correlation_heatmap.png,Variables thalach and slope are redundant.,3407
heart_correlation_heatmap.png,Variables oldpeak and slope are redundant.,3408
heart_correlation_heatmap.png,Variables ca and slope are redundant.,3409
heart_correlation_heatmap.png,Variables thal and slope are redundant.,3410
heart_correlation_heatmap.png,Variables age and ca are redundant.,3411
heart_correlation_heatmap.png,Variables cp and ca are redundant.,3412
heart_correlation_heatmap.png,Variables trestbps and ca are redundant.,3413
heart_correlation_heatmap.png,Variables chol and ca are redundant.,3414
heart_correlation_heatmap.png,Variables restecg and ca are redundant.,3415
heart_correlation_heatmap.png,Variables thalach and ca are redundant.,3416
heart_correlation_heatmap.png,Variables oldpeak and ca are redundant.,3417
heart_correlation_heatmap.png,Variables slope and ca are redundant.,3418
heart_correlation_heatmap.png,Variables thal and ca are redundant.,3419
heart_correlation_heatmap.png,Variables age and thal are redundant.,3420
heart_correlation_heatmap.png,Variables cp and thal are redundant.,3421
heart_correlation_heatmap.png,Variables trestbps and thal are redundant.,3422
heart_correlation_heatmap.png,Variables chol and thal are redundant.,3423
heart_correlation_heatmap.png,Variables restecg and thal are redundant.,3424
heart_correlation_heatmap.png,Variables oldpeak and thal are redundant.,3425
heart_correlation_heatmap.png,Variables slope and thal are redundant.,3426
heart_correlation_heatmap.png,Variables ca and thal are redundant.,3427
heart_correlation_heatmap.png,"Variables trestbps and slope are redundant, but we can’t say the same for the pair sex and chol.",3428
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair age and thal.",3429
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair age and thal.",3430
heart_correlation_heatmap.png,"Variables chol and slope are redundant, but we can’t say the same for the pair trestbps and oldpeak.",3431
heart_correlation_heatmap.png,"Variables age and oldpeak are redundant, but we can’t say the same for the pair ca and thal.",3432
heart_correlation_heatmap.png,"Variables age and trestbps are redundant, but we can’t say the same for the pair cp and ca.",3433
heart_correlation_heatmap.png,"Variables age and trestbps are redundant, but we can’t say the same for the pair thalach and slope.",3434
heart_correlation_heatmap.png,"Variables sex and thal are redundant, but we can’t say the same for the pair restecg and oldpeak.",3435
heart_correlation_heatmap.png,"Variables trestbps and chol are redundant, but we can’t say the same for the pair sex and thal.",3436
heart_correlation_heatmap.png,"Variables sex and trestbps are redundant, but we can’t say the same for the pair slope and thal.",3437
heart_correlation_heatmap.png,"Variables trestbps and exang are redundant, but we can’t say the same for the pair thalach and oldpeak.",3438
heart_correlation_heatmap.png,"Variables sex and restecg are redundant, but we can’t say the same for the pair age and thal.",3439
heart_correlation_heatmap.png,"Variables cp and chol are redundant, but we can’t say the same for the pair fbs and slope.",3440
heart_correlation_heatmap.png,"Variables sex and thal are redundant, but we can’t say the same for the pair exang and slope.",3441
heart_correlation_heatmap.png,"Variables trestbps and chol are redundant, but we can’t say the same for the pair thalach and ca.",3442
heart_correlation_heatmap.png,"Variables age and thalach are redundant, but we can’t say the same for the pair sex and trestbps.",3443
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair cp and chol.",3444
heart_correlation_heatmap.png,"Variables chol and fbs are redundant, but we can’t say the same for the pair age and thal.",3445
heart_correlation_heatmap.png,"Variables oldpeak and slope are redundant, but we can’t say the same for the pair fbs and exang.",3446
heart_correlation_heatmap.png,"Variables trestbps and exang are redundant, but we can’t say the same for the pair fbs and thalach.",3447
heart_correlation_heatmap.png,"Variables chol and thal are redundant, but we can’t say the same for the pair sex and slope.",3448
heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair restecg and oldpeak.",3449
heart_correlation_heatmap.png,"Variables restecg and thal are redundant, but we can’t say the same for the pair chol and oldpeak.",3450
heart_correlation_heatmap.png,"Variables fbs and thal are redundant, but we can’t say the same for the pair restecg and slope.",3451
heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair age and chol.",3452
heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair age and trestbps.",3453
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair fbs and exang.",3454
heart_correlation_heatmap.png,"Variables chol and fbs are redundant, but we can’t say the same for the pair age and restecg.",3455
heart_correlation_heatmap.png,"Variables restecg and oldpeak are redundant, but we can’t say the same for the pair cp and exang.",3456
heart_correlation_heatmap.png,"Variables sex and exang are redundant, but we can’t say the same for the pair age and thalach.",3457
heart_correlation_heatmap.png,"Variables chol and ca are redundant, but we can’t say the same for the pair cp and thalach.",3458
heart_correlation_heatmap.png,"Variables chol and oldpeak are redundant, but we can’t say the same for the pair ca and thal.",3459
heart_correlation_heatmap.png,"Variables cp and chol are redundant, but we can’t say the same for the pair age and restecg.",3460
heart_correlation_heatmap.png,"Variables cp and exang are redundant, but we can’t say the same for the pair thalach and ca.",3461
heart_correlation_heatmap.png,"Variables sex and fbs are redundant, but we can’t say the same for the pair restecg and thal.",3462
heart_correlation_heatmap.png,"Variables sex and fbs are redundant, but we can’t say the same for the pair cp and restecg.",3463
heart_correlation_heatmap.png,"Variables sex and trestbps are redundant, but we can’t say the same for the pair chol and ca.",3464
heart_correlation_heatmap.png,"Variables sex and trestbps are redundant, but we can’t say the same for the pair age and fbs.",3465
heart_correlation_heatmap.png,"Variables age and cp are redundant, but we can’t say the same for the pair thalach and exang.",3466
heart_correlation_heatmap.png,"Variables sex and trestbps are redundant, but we can’t say the same for the pair chol and fbs.",3467
heart_correlation_heatmap.png,"Variables chol and oldpeak are redundant, but we can’t say the same for the pair cp and slope.",3468
heart_correlation_heatmap.png,"Variables cp and thal are redundant, but we can’t say the same for the pair trestbps and ca.",3469
heart_correlation_heatmap.png,"Variables chol and fbs are redundant, but we can’t say the same for the pair cp and ca.",3470
heart_correlation_heatmap.png,"Variables thalach and thal are redundant, but we can’t say the same for the pair chol and slope.",3471
heart_correlation_heatmap.png,"Variables restecg and thalach are redundant, but we can’t say the same for the pair fbs and slope.",3472
heart_correlation_heatmap.png,"Variables chol and exang are redundant, but we can’t say the same for the pair restecg and ca.",3473
heart_correlation_heatmap.png,"Variables cp and exang are redundant, but we can’t say the same for the pair chol and slope.",3474
heart_correlation_heatmap.png,"Variables exang and oldpeak are redundant, but we can’t say the same for the pair slope and ca.",3475
heart_correlation_heatmap.png,"Variables chol and slope are redundant, but we can’t say the same for the pair age and trestbps.",3476
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair chol and oldpeak.",3477
heart_correlation_heatmap.png,"Variables sex and chol are redundant, but we can’t say the same for the pair trestbps and exang.",3478
heart_correlation_heatmap.png,"Variables age and ca are redundant, but we can’t say the same for the pair thalach and exang.",3479
heart_correlation_heatmap.png,"Variables oldpeak and thal are redundant, but we can’t say the same for the pair fbs and thalach.",3480
heart_correlation_heatmap.png,"Variables age and restecg are redundant, but we can’t say the same for the pair sex and trestbps.",3481
heart_correlation_heatmap.png,"Variables age and thal are redundant, but we can’t say the same for the pair sex and exang.",3482
heart_correlation_heatmap.png,"Variables restecg and oldpeak are redundant, but we can’t say the same for the pair trestbps and exang.",3483
heart_correlation_heatmap.png,"Variables fbs and ca are redundant, but we can’t say the same for the pair sex and restecg.",3484
heart_correlation_heatmap.png,"Variables cp and ca are redundant, but we can’t say the same for the pair trestbps and restecg.",3485
heart_correlation_heatmap.png,"Variables age and restecg are redundant, but we can’t say the same for the pair thalach and exang.",3486
heart_correlation_heatmap.png,"Variables exang and ca are redundant, but we can’t say the same for the pair age and fbs.",3487
heart_correlation_heatmap.png,"Variables cp and restecg are redundant, but we can’t say the same for the pair fbs and thal.",3488
heart_correlation_heatmap.png,"Variables sex and thal are redundant, but we can’t say the same for the pair age and thalach.",3489
heart_correlation_heatmap.png,"Variables ca and thal are redundant, but we can’t say the same for the pair age and chol.",3490
heart_correlation_heatmap.png,"Variables sex and oldpeak are redundant, but we can’t say the same for the pair age and cp.",3491
heart_correlation_heatmap.png,"Variables exang and slope are redundant, but we can’t say the same for the pair age and thal.",3492
heart_correlation_heatmap.png,"Variables thalach and thal are redundant, but we can’t say the same for the pair chol and exang.",3493
heart_correlation_heatmap.png,"Variables sex and thalach are redundant, but we can’t say the same for the pair restecg and exang.",3494
heart_correlation_heatmap.png,"Variables chol and thal are redundant, but we can’t say the same for the pair thalach and exang.",3495
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair cp and fbs.",3496
heart_correlation_heatmap.png,"Variables restecg and ca are redundant, but we can’t say the same for the pair age and oldpeak.",3497
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair thalach and oldpeak.",3498
heart_correlation_heatmap.png,"Variables thalach and oldpeak are redundant, but we can’t say the same for the pair chol and fbs.",3499
heart_correlation_heatmap.png,"Variables exang and slope are redundant, but we can’t say the same for the pair sex and oldpeak.",3500
heart_correlation_heatmap.png,"Variables age and oldpeak are redundant, but we can’t say the same for the pair chol and thalach.",3501
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair sex and exang.",3502
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair sex and chol.",3503
heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair fbs and exang.",3504
heart_correlation_heatmap.png,"Variables age and restecg are redundant, but we can’t say the same for the pair cp and fbs.",3505
heart_correlation_heatmap.png,"Variables sex and thalach are redundant, but we can’t say the same for the pair exang and thal.",3506
heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair age and chol.",3507
heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair age and thal.",3508
heart_correlation_heatmap.png,"Variables ca and thal are redundant, but we can’t say the same for the pair trestbps and chol.",3509
heart_correlation_heatmap.png,"Variables fbs and restecg are redundant, but we can’t say the same for the pair trestbps and thal.",3510
heart_correlation_heatmap.png,"Variables cp and thalach are redundant, but we can’t say the same for the pair sex and restecg.",3511
heart_correlation_heatmap.png,"Variables cp and ca are redundant, but we can’t say the same for the pair trestbps and slope.",3512
heart_correlation_heatmap.png,"Variables oldpeak and thal are redundant, but we can’t say the same for the pair cp and fbs.",3513
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair restecg and thalach.",3514
heart_correlation_heatmap.png,"Variables cp and exang are redundant, but we can’t say the same for the pair sex and slope.",3515
heart_correlation_heatmap.png,"Variables chol and ca are redundant, but we can’t say the same for the pair restecg and thalach.",3516
heart_correlation_heatmap.png,"Variables chol and ca are redundant, but we can’t say the same for the pair sex and cp.",3517
heart_correlation_heatmap.png,"Variables age and chol are redundant, but we can’t say the same for the pair thalach and exang.",3518
heart_correlation_heatmap.png,"Variables thalach and exang are redundant, but we can’t say the same for the pair cp and ca.",3519
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair exang and ca.",3520
heart_correlation_heatmap.png,"Variables sex and cp are redundant, but we can’t say the same for the pair age and thal.",3521
heart_correlation_heatmap.png,"Variables chol and ca are redundant, but we can’t say the same for the pair oldpeak and thal.",3522
heart_correlation_heatmap.png,"Variables trestbps and fbs are redundant, but we can’t say the same for the pair cp and thal.",3523
heart_correlation_heatmap.png,"Variables sex and thal are redundant, but we can’t say the same for the pair fbs and thalach.",3524
heart_correlation_heatmap.png,"Variables fbs and restecg are redundant, but we can’t say the same for the pair cp and oldpeak.",3525
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair slope and ca.",3526
heart_correlation_heatmap.png,"Variables sex and fbs are redundant, but we can’t say the same for the pair cp and chol.",3527
heart_correlation_heatmap.png,"Variables trestbps and exang are redundant, but we can’t say the same for the pair fbs and slope.",3528
heart_correlation_heatmap.png,"Variables age and restecg are redundant, but we can’t say the same for the pair cp and exang.",3529
heart_correlation_heatmap.png,"Variables trestbps and oldpeak are redundant, but we can’t say the same for the pair thalach and ca.",3530
heart_correlation_heatmap.png,"Variables trestbps and thal are redundant, but we can’t say the same for the pair cp and exang.",3531
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair fbs and oldpeak.",3532
heart_correlation_heatmap.png,"Variables cp and chol are redundant, but we can’t say the same for the pair thalach and exang.",3533
heart_correlation_heatmap.png,"Variables chol and exang are redundant, but we can’t say the same for the pair trestbps and thalach.",3534
heart_correlation_heatmap.png,"Variables age and ca are redundant, but we can’t say the same for the pair trestbps and thalach.",3535
heart_correlation_heatmap.png,"Variables sex and ca are redundant, but we can’t say the same for the pair age and slope.",3536
heart_correlation_heatmap.png,"Variables trestbps and slope are redundant, but we can’t say the same for the pair fbs and exang.",3537
heart_correlation_heatmap.png,"Variables trestbps and thalach are redundant, but we can’t say the same for the pair chol and exang.",3538
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair trestbps and thal.",3539
heart_correlation_heatmap.png,"Variables fbs and thal are redundant, but we can’t say the same for the pair cp and exang.",3540
heart_correlation_heatmap.png,"Variables oldpeak and slope are redundant, but we can’t say the same for the pair chol and fbs.",3541
heart_correlation_heatmap.png,"Variables cp and trestbps are redundant, but we can’t say the same for the pair restecg and thalach.",3542
heart_correlation_heatmap.png,"Variables sex and ca are redundant, but we can’t say the same for the pair fbs and thalach.",3543
heart_correlation_heatmap.png,"Variables restecg and thalach are redundant, but we can’t say the same for the pair oldpeak and slope.",3544
heart_correlation_heatmap.png,"Variables trestbps and thalach are redundant, but we can’t say the same for the pair exang and thal.",3545
heart_correlation_heatmap.png,"Variables age and cp are redundant, but we can’t say the same for the pair thalach and ca.",3546
heart_correlation_heatmap.png,"Variables age and sex are redundant, but we can’t say the same for the pair cp and exang.",3547
heart_correlation_heatmap.png,"Variables fbs and exang are redundant, but we can’t say the same for the pair thalach and ca.",3548
heart_correlation_heatmap.png,"Variables trestbps and oldpeak are redundant, but we can’t say the same for the pair age and restecg.",3549
heart_correlation_heatmap.png,"Variables cp and ca are redundant, but we can’t say the same for the pair fbs and restecg.",3550
heart_correlation_heatmap.png,"Variables exang and thal are redundant, but we can’t say the same for the pair trestbps and fbs.",3551
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair restecg and slope.",3552
heart_correlation_heatmap.png,"Variables cp and chol are redundant, but we can’t say the same for the pair age and trestbps.",3553
heart_correlation_heatmap.png,"Variables age and slope are redundant, but we can’t say the same for the pair trestbps and exang.",3554
heart_correlation_heatmap.png,"Variables sex and ca are redundant, but we can’t say the same for the pair cp and restecg.",3555
heart_correlation_heatmap.png,"Variables age and trestbps are redundant, but we can’t say the same for the pair fbs and slope.",3556
heart_correlation_heatmap.png,"Variables trestbps and chol are redundant, but we can’t say the same for the pair age and slope.",3557
heart_correlation_heatmap.png,"Variables cp and trestbps are redundant, but we can’t say the same for the pair sex and chol.",3558
heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair fbs and thalach.",3559
heart_correlation_heatmap.png,"Variables thalach and slope are redundant, but we can’t say the same for the pair age and restecg.",3560
heart_correlation_heatmap.png,"Variables sex and oldpeak are redundant, but we can’t say the same for the pair thalach and slope.",3561
heart_correlation_heatmap.png,"Variables fbs and exang are redundant, but we can’t say the same for the pair cp and restecg.",3562
heart_correlation_heatmap.png,"Variables sex and thal are redundant, but we can’t say the same for the pair restecg and thalach.",3563
heart_correlation_heatmap.png,"Variables age and fbs are redundant, but we can’t say the same for the pair sex and ca.",3564
heart_correlation_heatmap.png,"Variables cp and ca are redundant, but we can’t say the same for the pair trestbps and exang.",3565
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair chol and restecg.",3566
heart_correlation_heatmap.png,"Variables sex and thalach are redundant, but we can’t say the same for the pair trestbps and exang.",3567
heart_correlation_heatmap.png,"Variables sex and fbs are redundant, but we can’t say the same for the pair chol and slope.",3568
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair sex and chol.",3569
heart_correlation_heatmap.png,"Variables thalach and slope are redundant, but we can’t say the same for the pair chol and fbs.",3570
heart_correlation_heatmap.png,"Variables age and ca are redundant, but we can’t say the same for the pair exang and thal.",3571
heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair chol and restecg.",3572
heart_correlation_heatmap.png,"Variables sex and chol are redundant, but we can’t say the same for the pair oldpeak and ca.",3573
heart_correlation_heatmap.png,"Variables sex and oldpeak are redundant, but we can’t say the same for the pair cp and exang.",3574
heart_correlation_heatmap.png,"Variables thalach and slope are redundant, but we can’t say the same for the pair age and exang.",3575
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair chol and exang.",3576
heart_correlation_heatmap.png,"Variables chol and ca are redundant, but we can’t say the same for the pair fbs and thalach.",3577
heart_correlation_heatmap.png,"Variables age and trestbps are redundant, but we can’t say the same for the pair chol and exang.",3578
heart_correlation_heatmap.png,"Variables oldpeak and thal are redundant, but we can’t say the same for the pair restecg and ca.",3579
heart_correlation_heatmap.png,"Variables fbs and thalach are redundant, but we can’t say the same for the pair exang and oldpeak.",3580
heart_correlation_heatmap.png,"Variables chol and slope are redundant, but we can’t say the same for the pair age and restecg.",3581
heart_correlation_heatmap.png,"Variables fbs and restecg are redundant, but we can’t say the same for the pair age and oldpeak.",3582
heart_correlation_heatmap.png,"Variables slope and ca are redundant, but we can’t say the same for the pair fbs and oldpeak.",3583
heart_correlation_heatmap.png,"Variables chol and exang are redundant, but we can’t say the same for the pair age and sex.",3584
heart_correlation_heatmap.png,"Variables age and trestbps are redundant, but we can’t say the same for the pair chol and slope.",3585
heart_correlation_heatmap.png,"Variables cp and trestbps are redundant, but we can’t say the same for the pair sex and restecg.",3586
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair age and fbs.",3587
heart_correlation_heatmap.png,"Variables trestbps and slope are redundant, but we can’t say the same for the pair restecg and exang.",3588
heart_correlation_heatmap.png,"Variables exang and ca are redundant, but we can’t say the same for the pair restecg and slope.",3589
heart_correlation_heatmap.png,"Variables chol and exang are redundant, but we can’t say the same for the pair thalach and oldpeak.",3590
heart_correlation_heatmap.png,"Variables cp and oldpeak are redundant, but we can’t say the same for the pair thalach and ca.",3591
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair age and sex.",3592
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair trestbps and exang.",3593
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair exang and ca.",3594
heart_correlation_heatmap.png,"Variables age and thal are redundant, but we can’t say the same for the pair restecg and slope.",3595
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair restecg and exang.",3596
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair exang and slope.",3597
heart_correlation_heatmap.png,"Variables exang and oldpeak are redundant, but we can’t say the same for the pair sex and trestbps.",3598
heart_correlation_heatmap.png,"Variables restecg and ca are redundant, but we can’t say the same for the pair sex and trestbps.",3599
heart_correlation_heatmap.png,"Variables thalach and exang are redundant, but we can’t say the same for the pair sex and chol.",3600
heart_correlation_heatmap.png,"Variables chol and slope are redundant, but we can’t say the same for the pair restecg and ca.",3601
heart_correlation_heatmap.png,"Variables restecg and oldpeak are redundant, but we can’t say the same for the pair age and ca.",3602
heart_correlation_heatmap.png,"Variables sex and restecg are redundant, but we can’t say the same for the pair cp and oldpeak.",3603
heart_correlation_heatmap.png,"Variables sex and ca are redundant, but we can’t say the same for the pair age and thal.",3604
heart_correlation_heatmap.png,"Variables fbs and thal are redundant, but we can’t say the same for the pair trestbps and restecg.",3605
heart_correlation_heatmap.png,"Variables thalach and exang are redundant, but we can’t say the same for the pair restecg and slope.",3606
heart_correlation_heatmap.png,"Variables fbs and exang are redundant, but we can’t say the same for the pair oldpeak and slope.",3607
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair cp and thal.",3608
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair fbs and thal.",3609
heart_correlation_heatmap.png,"Variables exang and oldpeak are redundant, but we can’t say the same for the pair age and slope.",3610
heart_correlation_heatmap.png,"Variables thalach and slope are redundant, but we can’t say the same for the pair cp and oldpeak.",3611
heart_correlation_heatmap.png,"Variables exang and thal are redundant, but we can’t say the same for the pair thalach and slope.",3612
heart_correlation_heatmap.png,"Variables sex and thalach are redundant, but we can’t say the same for the pair trestbps and ca.",3613
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair age and ca.",3614
heart_correlation_heatmap.png,"Variables exang and thal are redundant, but we can’t say the same for the pair fbs and ca.",3615
heart_correlation_heatmap.png,"Variables restecg and thal are redundant, but we can’t say the same for the pair sex and ca.",3616
heart_correlation_heatmap.png,"Variables slope and ca are redundant, but we can’t say the same for the pair cp and chol.",3617
heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair chol and fbs.",3618
heart_correlation_heatmap.png,"Variables oldpeak and slope are redundant, but we can’t say the same for the pair chol and exang.",3619
heart_correlation_heatmap.png,"Variables thalach and thal are redundant, but we can’t say the same for the pair exang and oldpeak.",3620
heart_correlation_heatmap.png,"Variables cp and fbs are redundant, but we can’t say the same for the pair thalach and ca.",3621
heart_correlation_heatmap.png,"Variables sex and exang are redundant, but we can’t say the same for the pair fbs and thal.",3622
heart_correlation_heatmap.png,"Variables fbs and thal are redundant, but we can’t say the same for the pair restecg and thalach.",3623
heart_correlation_heatmap.png,"Variables trestbps and restecg are redundant, but we can’t say the same for the pair age and thal.",3624
heart_correlation_heatmap.png,"Variables age and sex are redundant, but we can’t say the same for the pair chol and fbs.",3625
heart_correlation_heatmap.png,"Variables trestbps and restecg are redundant, but we can’t say the same for the pair age and thalach.",3626
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair oldpeak and thal.",3627
heart_correlation_heatmap.png,"Variables chol and fbs are redundant, but we can’t say the same for the pair trestbps and slope.",3628
heart_correlation_heatmap.png,"Variables cp and fbs are redundant, but we can’t say the same for the pair restecg and oldpeak.",3629
heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair cp and restecg.",3630
heart_correlation_heatmap.png,"Variables trestbps and thalach are redundant, but we can’t say the same for the pair chol and ca.",3631
heart_correlation_heatmap.png,"Variables fbs and slope are redundant, but we can’t say the same for the pair age and trestbps.",3632
heart_correlation_heatmap.png,"Variables age and thalach are redundant, but we can’t say the same for the pair cp and thal.",3633
heart_correlation_heatmap.png,"Variables restecg and ca are redundant, but we can’t say the same for the pair sex and thal.",3634
heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair sex and exang.",3635
heart_correlation_heatmap.png,"Variables chol and slope are redundant, but we can’t say the same for the pair thalach and exang.",3636
heart_correlation_heatmap.png,"Variables trestbps and slope are redundant, but we can’t say the same for the pair chol and thalach.",3637
heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair ca and thal.",3638
heart_correlation_heatmap.png,"Variables sex and fbs are redundant, but we can’t say the same for the pair cp and oldpeak.",3639
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair age and restecg.",3640
heart_correlation_heatmap.png,"Variables trestbps and fbs are redundant, but we can’t say the same for the pair thalach and exang.",3641
heart_correlation_heatmap.png,"Variables trestbps and slope are redundant, but we can’t say the same for the pair fbs and oldpeak.",3642
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair fbs and ca.",3643
heart_correlation_heatmap.png,"Variables cp and restecg are redundant, but we can’t say the same for the pair sex and fbs.",3644
heart_correlation_heatmap.png,"Variables chol and fbs are redundant, but we can’t say the same for the pair cp and thalach.",3645
heart_correlation_heatmap.png,"Variables fbs and thal are redundant, but we can’t say the same for the pair sex and thalach.",3646
heart_correlation_heatmap.png,"Variables age and cp are redundant, but we can’t say the same for the pair restecg and slope.",3647
heart_correlation_heatmap.png,"Variables slope and ca are redundant, but we can’t say the same for the pair oldpeak and thal.",3648
heart_correlation_heatmap.png,"Variables chol and oldpeak are redundant, but we can’t say the same for the pair restecg and exang.",3649
heart_correlation_heatmap.png,"Variables trestbps and restecg are redundant, but we can’t say the same for the pair thalach and oldpeak.",3650
heart_correlation_heatmap.png,"Variables chol and thalach are redundant, but we can’t say the same for the pair cp and fbs.",3651
heart_correlation_heatmap.png,"Variables trestbps and restecg are redundant, but we can’t say the same for the pair exang and ca.",3652
heart_correlation_heatmap.png,"Variables sex and thal are redundant, but we can’t say the same for the pair chol and oldpeak.",3653
heart_correlation_heatmap.png,"Variables cp and thalach are redundant, but we can’t say the same for the pair restecg and thal.",3654
heart_correlation_heatmap.png,"Variables trestbps and oldpeak are redundant, but we can’t say the same for the pair age and fbs.",3655
heart_correlation_heatmap.png,"Variables fbs and restecg are redundant, but we can’t say the same for the pair age and cp.",3656
heart_correlation_heatmap.png,"Variables restecg and exang are redundant, but we can’t say the same for the pair fbs and thalach.",3657
heart_correlation_heatmap.png,"Variables trestbps and fbs are redundant, but we can’t say the same for the pair ca and thal.",3658
heart_correlation_heatmap.png,"Variables chol and thal are redundant, but we can’t say the same for the pair age and trestbps.",3659
heart_correlation_heatmap.png,"Variables sex and oldpeak are redundant, but we can’t say the same for the pair cp and trestbps.",3660
heart_correlation_heatmap.png,"Variables cp and trestbps are redundant, but we can’t say the same for the pair chol and fbs.",3661
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair sex and chol.",3662
heart_correlation_heatmap.png,"Variables sex and exang are redundant, but we can’t say the same for the pair trestbps and fbs.",3663
heart_correlation_heatmap.png,"Variables chol and oldpeak are redundant, but we can’t say the same for the pair trestbps and thalach.",3664
heart_correlation_heatmap.png,"Variables cp and thal are redundant, but we can’t say the same for the pair restecg and ca.",3665
heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair restecg and exang.",3666
heart_correlation_heatmap.png,"Variables trestbps and slope are redundant, but we can’t say the same for the pair cp and thal.",3667
heart_correlation_heatmap.png,"Variables restecg and ca are redundant, but we can’t say the same for the pair thalach and exang.",3668
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair thalach and exang.",3669
heart_correlation_heatmap.png,"Variables fbs and oldpeak are redundant, but we can’t say the same for the pair thalach and slope.",3670
heart_correlation_heatmap.png,"Variables sex and oldpeak are redundant, but we can’t say the same for the pair age and trestbps.",3671
heart_correlation_heatmap.png,"Variables thalach and exang are redundant, but we can’t say the same for the pair chol and thal.",3672
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair sex and thal.",3673
heart_correlation_heatmap.png,"Variables exang and slope are redundant, but we can’t say the same for the pair age and chol.",3674
heart_correlation_heatmap.png,"Variables age and oldpeak are redundant, but we can’t say the same for the pair trestbps and exang.",3675
heart_correlation_heatmap.png,"Variables cp and thalach are redundant, but we can’t say the same for the pair restecg and ca.",3676
heart_correlation_heatmap.png,"Variables age and thalach are redundant, but we can’t say the same for the pair fbs and slope.",3677
heart_correlation_heatmap.png,"Variables sex and trestbps are redundant, but we can’t say the same for the pair fbs and ca.",3678
heart_correlation_heatmap.png,"Variables thalach and exang are redundant, but we can’t say the same for the pair age and fbs.",3679
heart_correlation_heatmap.png,"Variables oldpeak and ca are redundant, but we can’t say the same for the pair restecg and thal.",3680
heart_correlation_heatmap.png,"Variables fbs and exang are redundant, but we can’t say the same for the pair sex and thal.",3681
heart_correlation_heatmap.png,"Variables thalach and ca are redundant, but we can’t say the same for the pair cp and thal.",3682
heart_correlation_heatmap.png,"Variables thalach and ca are redundant, but we can’t say the same for the pair chol and slope.",3683
heart_correlation_heatmap.png,"Variables chol and oldpeak are redundant, but we can’t say the same for the pair fbs and exang.",3684
heart_correlation_heatmap.png,"Variables chol and thalach are redundant, but we can’t say the same for the pair cp and trestbps.",3685
heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair trestbps and ca.",3686
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair age and thalach.",3687
heart_correlation_heatmap.png,"Variables fbs and slope are redundant, but we can’t say the same for the pair chol and thalach.",3688
heart_correlation_heatmap.png,"Variables trestbps and thal are redundant, but we can’t say the same for the pair age and oldpeak.",3689
heart_correlation_heatmap.png,"Variables age and ca are redundant, but we can’t say the same for the pair slope and thal.",3690
heart_correlation_heatmap.png,"Variables cp and trestbps are redundant, but we can’t say the same for the pair chol and thal.",3691
heart_correlation_heatmap.png,"Variables trestbps and chol are redundant, but we can’t say the same for the pair age and oldpeak.",3692
heart_correlation_heatmap.png,"Variables oldpeak and thal are redundant, but we can’t say the same for the pair sex and ca.",3693
heart_correlation_heatmap.png,"Variables chol and thalach are redundant, but we can’t say the same for the pair oldpeak and slope.",3694
heart_correlation_heatmap.png,"Variables trestbps and thal are redundant, but we can’t say the same for the pair restecg and exang.",3695
heart_correlation_heatmap.png,"Variables cp and exang are redundant, but we can’t say the same for the pair chol and fbs.",3696
heart_correlation_heatmap.png,"Variables chol and thalach are redundant, but we can’t say the same for the pair age and oldpeak.",3697
heart_correlation_heatmap.png,"Variables chol and thalach are redundant, but we can’t say the same for the pair slope and thal.",3698
heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair restecg and oldpeak.",3699
heart_correlation_heatmap.png,"Variables fbs and ca are redundant, but we can’t say the same for the pair cp and slope.",3700
heart_correlation_heatmap.png,"Variables fbs and slope are redundant, but we can’t say the same for the pair sex and chol.",3701
heart_correlation_heatmap.png,"Variables restecg and slope are redundant, but we can’t say the same for the pair trestbps and exang.",3702
heart_correlation_heatmap.png,"Variables restecg and ca are redundant, but we can’t say the same for the pair sex and cp.",3703
heart_correlation_heatmap.png,"Variables age and oldpeak are redundant, but we can’t say the same for the pair thalach and ca.",3704
heart_correlation_heatmap.png,"Variables trestbps and exang are redundant, but we can’t say the same for the pair age and slope.",3705
heart_correlation_heatmap.png,"Variables cp and exang are redundant, but we can’t say the same for the pair chol and ca.",3706
heart_correlation_heatmap.png,"Variables cp and restecg are redundant, but we can’t say the same for the pair exang and slope.",3707
heart_correlation_heatmap.png,"Variables chol and oldpeak are redundant, but we can’t say the same for the pair sex and ca.",3708
heart_correlation_heatmap.png,"Variables age and ca are redundant, but we can’t say the same for the pair cp and fbs.",3709
heart_correlation_heatmap.png,"Variables fbs and slope are redundant, but we can’t say the same for the pair cp and ca.",3710
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair sex and thalach.",3711
heart_correlation_heatmap.png,"Variables age and trestbps are redundant, but we can’t say the same for the pair thalach and ca.",3712
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair exang and slope.",3713
heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair sex and exang.",3714
heart_correlation_heatmap.png,"Variables fbs and slope are redundant, but we can’t say the same for the pair sex and thalach.",3715
heart_correlation_heatmap.png,"Variables age and sex are redundant, but we can’t say the same for the pair chol and oldpeak.",3716
heart_correlation_heatmap.png,"Variables fbs and restecg are redundant, but we can’t say the same for the pair age and sex.",3717
heart_correlation_heatmap.png,"Variables cp and fbs are redundant, but we can’t say the same for the pair chol and exang.",3718
heart_correlation_heatmap.png,"Variables cp and oldpeak are redundant, but we can’t say the same for the pair slope and ca.",3719
heart_correlation_heatmap.png,"Variables sex and thal are redundant, but we can’t say the same for the pair thalach and oldpeak.",3720
heart_correlation_heatmap.png,"Variables restecg and oldpeak are redundant, but we can’t say the same for the pair exang and slope.",3721
heart_correlation_heatmap.png,"Variables restecg and oldpeak are redundant, but we can’t say the same for the pair thalach and slope.",3722
heart_correlation_heatmap.png,"Variables cp and restecg are redundant, but we can’t say the same for the pair trestbps and thalach.",3723
heart_correlation_heatmap.png,"Variables fbs and restecg are redundant, but we can’t say the same for the pair trestbps and chol.",3724
heart_correlation_heatmap.png,"Variables cp and restecg are redundant, but we can’t say the same for the pair age and oldpeak.",3725
heart_correlation_heatmap.png,"Variables age and restecg are redundant, but we can’t say the same for the pair trestbps and exang.",3726
heart_correlation_heatmap.png,"Variables restecg and exang are redundant, but we can’t say the same for the pair trestbps and slope.",3727
heart_correlation_heatmap.png,"Variables exang and slope are redundant, but we can’t say the same for the pair ca and thal.",3728
heart_correlation_heatmap.png,"Variables trestbps and restecg are redundant, but we can’t say the same for the pair age and exang.",3729
heart_correlation_heatmap.png,"Variables fbs and thal are redundant, but we can’t say the same for the pair age and trestbps.",3730
heart_correlation_heatmap.png,"Variables exang and thal are redundant, but we can’t say the same for the pair cp and fbs.",3731
heart_correlation_heatmap.png,"Variables cp and chol are redundant, but we can’t say the same for the pair trestbps and ca.",3732
heart_correlation_heatmap.png,"Variables sex and exang are redundant, but we can’t say the same for the pair cp and oldpeak.",3733
heart_correlation_heatmap.png,"Variables sex and oldpeak are redundant, but we can’t say the same for the pair slope and thal.",3734
heart_correlation_heatmap.png,"Variables thalach and thal are redundant, but we can’t say the same for the pair trestbps and oldpeak.",3735
heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair age and trestbps.",3736
heart_correlation_heatmap.png,"Variables age and fbs are redundant, but we can’t say the same for the pair trestbps and chol.",3737
heart_correlation_heatmap.png,"Variables age and restecg are redundant, but we can’t say the same for the pair oldpeak and slope.",3738
heart_correlation_heatmap.png,"Variables trestbps and exang are redundant, but we can’t say the same for the pair age and restecg.",3739
heart_correlation_heatmap.png,The variable age can be discarded without risking losing information.,3740
heart_correlation_heatmap.png,The variable cp can be discarded without risking losing information.,3741
heart_correlation_heatmap.png,The variable trestbps can be discarded without risking losing information.,3742
heart_correlation_heatmap.png,The variable chol can be discarded without risking losing information.,3743
heart_correlation_heatmap.png,The variable restecg can be discarded without risking losing information.,3744
heart_correlation_heatmap.png,The variable thalach can be discarded without risking losing information.,3745
heart_correlation_heatmap.png,The variable oldpeak can be discarded without risking losing information.,3746
heart_correlation_heatmap.png,The variable slope can be discarded without risking losing information.,3747
heart_correlation_heatmap.png,The variable ca can be discarded without risking losing information.,3748
heart_correlation_heatmap.png,The variable thal can be discarded without risking losing information.,3749
heart_correlation_heatmap.png,One of the variables cp or age can be discarded without losing information.,3750
heart_correlation_heatmap.png,One of the variables trestbps or age can be discarded without losing information.,3751
heart_correlation_heatmap.png,One of the variables chol or age can be discarded without losing information.,3752
heart_correlation_heatmap.png,One of the variables restecg or age can be discarded without losing information.,3753
heart_correlation_heatmap.png,One of the variables thalach or age can be discarded without losing information.,3754
heart_correlation_heatmap.png,One of the variables oldpeak or age can be discarded without losing information.,3755
heart_correlation_heatmap.png,One of the variables slope or age can be discarded without losing information.,3756
heart_correlation_heatmap.png,One of the variables ca or age can be discarded without losing information.,3757
heart_correlation_heatmap.png,One of the variables thal or age can be discarded without losing information.,3758
heart_correlation_heatmap.png,One of the variables age or cp can be discarded without losing information.,3759
heart_correlation_heatmap.png,One of the variables trestbps or cp can be discarded without losing information.,3760
heart_correlation_heatmap.png,One of the variables chol or cp can be discarded without losing information.,3761
heart_correlation_heatmap.png,One of the variables restecg or cp can be discarded without losing information.,3762
heart_correlation_heatmap.png,One of the variables thalach or cp can be discarded without losing information.,3763
heart_correlation_heatmap.png,One of the variables oldpeak or cp can be discarded without losing information.,3764
heart_correlation_heatmap.png,One of the variables slope or cp can be discarded without losing information.,3765
heart_correlation_heatmap.png,One of the variables ca or cp can be discarded without losing information.,3766
heart_correlation_heatmap.png,One of the variables thal or cp can be discarded without losing information.,3767
heart_correlation_heatmap.png,One of the variables age or trestbps can be discarded without losing information.,3768
heart_correlation_heatmap.png,One of the variables cp or trestbps can be discarded without losing information.,3769
heart_correlation_heatmap.png,One of the variables chol or trestbps can be discarded without losing information.,3770
heart_correlation_heatmap.png,One of the variables restecg or trestbps can be discarded without losing information.,3771
heart_correlation_heatmap.png,One of the variables thalach or trestbps can be discarded without losing information.,3772
heart_correlation_heatmap.png,One of the variables oldpeak or trestbps can be discarded without losing information.,3773
heart_correlation_heatmap.png,One of the variables slope or trestbps can be discarded without losing information.,3774
heart_correlation_heatmap.png,One of the variables ca or trestbps can be discarded without losing information.,3775
heart_correlation_heatmap.png,One of the variables thal or trestbps can be discarded without losing information.,3776
heart_correlation_heatmap.png,One of the variables age or chol can be discarded without losing information.,3777
heart_correlation_heatmap.png,One of the variables cp or chol can be discarded without losing information.,3778
heart_correlation_heatmap.png,One of the variables trestbps or chol can be discarded without losing information.,3779
heart_correlation_heatmap.png,One of the variables restecg or chol can be discarded without losing information.,3780
heart_correlation_heatmap.png,One of the variables thalach or chol can be discarded without losing information.,3781
heart_correlation_heatmap.png,One of the variables oldpeak or chol can be discarded without losing information.,3782
heart_correlation_heatmap.png,One of the variables slope or chol can be discarded without losing information.,3783
heart_correlation_heatmap.png,One of the variables ca or chol can be discarded without losing information.,3784
heart_correlation_heatmap.png,One of the variables thal or chol can be discarded without losing information.,3785
heart_correlation_heatmap.png,One of the variables age or restecg can be discarded without losing information.,3786
heart_correlation_heatmap.png,One of the variables cp or restecg can be discarded without losing information.,3787
heart_correlation_heatmap.png,One of the variables trestbps or restecg can be discarded without losing information.,3788
heart_correlation_heatmap.png,One of the variables chol or restecg can be discarded without losing information.,3789
heart_correlation_heatmap.png,One of the variables thalach or restecg can be discarded without losing information.,3790
heart_correlation_heatmap.png,One of the variables oldpeak or restecg can be discarded without losing information.,3791
heart_correlation_heatmap.png,One of the variables slope or restecg can be discarded without losing information.,3792
heart_correlation_heatmap.png,One of the variables ca or restecg can be discarded without losing information.,3793
heart_correlation_heatmap.png,One of the variables thal or restecg can be discarded without losing information.,3794
heart_correlation_heatmap.png,One of the variables age or thalach can be discarded without losing information.,3795
heart_correlation_heatmap.png,One of the variables cp or thalach can be discarded without losing information.,3796
heart_correlation_heatmap.png,One of the variables trestbps or thalach can be discarded without losing information.,3797
heart_correlation_heatmap.png,One of the variables chol or thalach can be discarded without losing information.,3798
heart_correlation_heatmap.png,One of the variables restecg or thalach can be discarded without losing information.,3799
heart_correlation_heatmap.png,One of the variables oldpeak or thalach can be discarded without losing information.,3800
heart_correlation_heatmap.png,One of the variables slope or thalach can be discarded without losing information.,3801
heart_correlation_heatmap.png,One of the variables ca or thalach can be discarded without losing information.,3802
heart_correlation_heatmap.png,One of the variables thal or thalach can be discarded without losing information.,3803
heart_correlation_heatmap.png,One of the variables age or oldpeak can be discarded without losing information.,3804
heart_correlation_heatmap.png,One of the variables cp or oldpeak can be discarded without losing information.,3805
heart_correlation_heatmap.png,One of the variables trestbps or oldpeak can be discarded without losing information.,3806
heart_correlation_heatmap.png,One of the variables chol or oldpeak can be discarded without losing information.,3807
heart_correlation_heatmap.png,One of the variables restecg or oldpeak can be discarded without losing information.,3808
heart_correlation_heatmap.png,One of the variables thalach or oldpeak can be discarded without losing information.,3809
heart_correlation_heatmap.png,One of the variables slope or oldpeak can be discarded without losing information.,3810
heart_correlation_heatmap.png,One of the variables ca or oldpeak can be discarded without losing information.,3811
heart_correlation_heatmap.png,One of the variables thal or oldpeak can be discarded without losing information.,3812
heart_correlation_heatmap.png,One of the variables age or slope can be discarded without losing information.,3813
heart_correlation_heatmap.png,One of the variables cp or slope can be discarded without losing information.,3814
heart_correlation_heatmap.png,One of the variables trestbps or slope can be discarded without losing information.,3815
heart_correlation_heatmap.png,One of the variables chol or slope can be discarded without losing information.,3816
heart_correlation_heatmap.png,One of the variables restecg or slope can be discarded without losing information.,3817
heart_correlation_heatmap.png,One of the variables thalach or slope can be discarded without losing information.,3818
heart_correlation_heatmap.png,One of the variables oldpeak or slope can be discarded without losing information.,3819
heart_correlation_heatmap.png,One of the variables ca or slope can be discarded without losing information.,3820
heart_correlation_heatmap.png,One of the variables thal or slope can be discarded without losing information.,3821
heart_correlation_heatmap.png,One of the variables age or thal can be discarded without losing information.,3822
heart_correlation_heatmap.png,One of the variables cp or thal can be discarded without losing information.,3823
heart_correlation_heatmap.png,One of the variables trestbps or thal can be discarded without losing information.,3824
heart_correlation_heatmap.png,One of the variables chol or thal can be discarded without losing information.,3825
heart_correlation_heatmap.png,One of the variables restecg or thal can be discarded without losing information.,3826
heart_correlation_heatmap.png,One of the variables oldpeak or thal can be discarded without losing information.,3827
heart_correlation_heatmap.png,One of the variables slope or thal can be discarded without losing information.,3828
heart_correlation_heatmap.png,One of the variables ca or thal can be discarded without losing information.,3829
heart_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,3830
heart_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,3831
heart_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,3832
heart_boxplots.png,At least 50 of the variables present outliers.,3833
heart_histograms_numeric.png,At least 60 of the variables present outliers.,3834
heart_boxplots.png,At least 75 of the variables present outliers.,3835
heart_histograms_numeric.png,At least 85 of the variables present outliers.,3836
heart_boxplots.png,Variable age presents some outliers.,3837
heart_boxplots.png,Variable cp presents some outliers.,3838
heart_boxplots.png,Variable trestbps presents some outliers.,3839
heart_boxplots.png,Variable chol presents some outliers.,3840
heart_boxplots.png,Variable restecg presents some outliers.,3841
heart_boxplots.png,Variable thalach presents some outliers.,3842
heart_histograms_numeric.png,Variable oldpeak presents some outliers.,3843
heart_boxplots.png,Variable slope presents some outliers.,3844
heart_boxplots.png,Variable ca presents some outliers.,3845
heart_boxplots.png,Variable thal presents some outliers.,3846
heart_boxplots.png,Variable age doesn’t have any outliers.,3847
heart_boxplots.png,Variable cp doesn’t have any outliers.,3848
heart_boxplots.png,Variable trestbps doesn’t have any outliers.,3849
heart_boxplots.png,Variable chol doesn’t have any outliers.,3850
heart_boxplots.png,Variable restecg doesn’t have any outliers.,3851
heart_histograms_numeric.png,Variable thalach doesn’t have any outliers.,3852
heart_histograms_numeric.png,Variable oldpeak doesn’t have any outliers.,3853
heart_boxplots.png,Variable slope doesn’t have any outliers.,3854
heart_boxplots.png,Variable ca doesn’t have any outliers.,3855
heart_boxplots.png,Variable thal doesn’t have any outliers.,3856
heart_boxplots.png,Variable age shows some outlier values.,3857
heart_histograms_numeric.png,Variable cp shows some outlier values.,3858
heart_boxplots.png,Variable trestbps shows some outlier values.,3859
heart_boxplots.png,Variable chol shows some outlier values.,3860
heart_boxplots.png,Variable restecg shows some outlier values.,3861
heart_histograms_numeric.png,Variable thalach shows some outlier values.,3862
heart_boxplots.png,Variable oldpeak shows some outlier values.,3863
heart_histograms_numeric.png,Variable slope shows some outlier values.,3864
heart_histograms_numeric.png,Variable ca shows some outlier values.,3865
heart_histograms_numeric.png,Variable thal shows some outlier values.,3866
heart_boxplots.png,Variable age shows a high number of outlier values.,3867
heart_histograms_numeric.png,Variable cp shows a high number of outlier values.,3868
heart_histograms_numeric.png,Variable trestbps shows a high number of outlier values.,3869
heart_histograms_numeric.png,Variable chol shows a high number of outlier values.,3870
heart_histograms_numeric.png,Variable restecg shows a high number of outlier values.,3871
heart_histograms_numeric.png,Variable thalach shows a high number of outlier values.,3872
heart_histograms_numeric.png,Variable oldpeak shows a high number of outlier values.,3873
heart_histograms_numeric.png,Variable slope shows a high number of outlier values.,3874
heart_boxplots.png,Variable ca shows a high number of outlier values.,3875
heart_boxplots.png,Variable thal shows a high number of outlier values.,3876
heart_histograms_numeric.png,Outliers seem to be a problem in the dataset.,3877
heart_histograms_numeric.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable age.",3878
heart_boxplots.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable age.",3879
heart_histograms_numeric.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable age.",3880
heart_boxplots.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable age.",3881
heart_histograms_numeric.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable age.",3882
heart_boxplots.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable age.",3883
heart_boxplots.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable age.",3884
heart_histograms_numeric.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable age.",3885
heart_histograms_numeric.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable age.",3886
heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable cp.",3887
heart_boxplots.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable cp.",3888
heart_histograms_numeric.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable cp.",3889
heart_boxplots.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable cp.",3890
heart_boxplots.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable cp.",3891
heart_histograms_numeric.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable cp.",3892
heart_boxplots.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable cp.",3893
heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable cp.",3894
heart_boxplots.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable cp.",3895
heart_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable trestbps.",3896
heart_boxplots.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable trestbps.",3897
heart_histograms_numeric.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable trestbps.",3898
heart_histograms_numeric.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable trestbps.",3899
heart_boxplots.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable trestbps.",3900
heart_histograms_numeric.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable trestbps.",3901
heart_boxplots.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable trestbps.",3902
heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable trestbps.",3903
heart_histograms_numeric.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable trestbps.",3904
heart_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable chol.",3905
heart_histograms_numeric.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable chol.",3906
heart_histograms_numeric.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable chol.",3907
heart_histograms_numeric.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable chol.",3908
heart_boxplots.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable chol.",3909
heart_boxplots.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable chol.",3910
heart_histograms_numeric.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable chol.",3911
heart_histograms_numeric.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable chol.",3912
heart_histograms_numeric.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable chol.",3913
heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable restecg.",3914
heart_boxplots.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable restecg.",3915
heart_boxplots.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable restecg.",3916
heart_boxplots.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable restecg.",3917
heart_histograms_numeric.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable restecg.",3918
heart_boxplots.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable restecg.",3919
heart_histograms_numeric.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable restecg.",3920
heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable restecg.",3921
heart_histograms_numeric.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable restecg.",3922
heart_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable thalach.",3923
heart_histograms_numeric.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable thalach.",3924
heart_boxplots.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable thalach.",3925
heart_histograms_numeric.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable thalach.",3926
heart_histograms_numeric.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable thalach.",3927
heart_histograms_numeric.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable thalach.",3928
heart_boxplots.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable thalach.",3929
heart_histograms_numeric.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable thalach.",3930
heart_histograms_numeric.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable thalach.",3931
heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable oldpeak.",3932
heart_boxplots.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable oldpeak.",3933
heart_histograms_numeric.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable oldpeak.",3934
heart_histograms_numeric.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable oldpeak.",3935
heart_boxplots.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable oldpeak.",3936
heart_histograms_numeric.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable oldpeak.",3937
heart_boxplots.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable oldpeak.",3938
heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable oldpeak.",3939
heart_boxplots.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable oldpeak.",3940
heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable slope.",3941
heart_boxplots.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable slope.",3942
heart_boxplots.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable slope.",3943
heart_boxplots.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable slope.",3944
heart_boxplots.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable slope.",3945
heart_histograms_numeric.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable slope.",3946
heart_histograms_numeric.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable slope.",3947
heart_histograms_numeric.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable slope.",3948
heart_boxplots.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable slope.",3949
heart_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable thal.",3950
heart_histograms_numeric.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable thal.",3951
heart_histograms_numeric.png,"It is clear that variable trestbps shows some outliers, but we can’t be sure of the same for variable thal.",3952
heart_histograms_numeric.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable thal.",3953
heart_boxplots.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable thal.",3954
heart_boxplots.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable thal.",3955
heart_histograms_numeric.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable thal.",3956
heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable thal.",3957
heart_boxplots.png,Those boxplots show that the data is not normalized.,3958
heart_histograms_numeric.png,Variable age is balanced.,3959
heart_boxplots.png,Variable cp is balanced.,3960
heart_boxplots.png,Variable trestbps is balanced.,3961
heart_boxplots.png,Variable chol is balanced.,3962
heart_histograms_numeric.png,Variable restecg is balanced.,3963
heart_histograms_numeric.png,Variable thalach is balanced.,3964
heart_histograms_numeric.png,Variable oldpeak is balanced.,3965
heart_boxplots.png,Variable slope is balanced.,3966
heart_boxplots.png,Variable ca is balanced.,3967
heart_histograms_numeric.png,Variable thal is balanced.,3968
heart_histograms.png,The variable age can be seen as ordinal without losing information.,3969
heart_histograms.png,The variable sex can be seen as ordinal without losing information.,3970
heart_histograms.png,The variable cp can be seen as ordinal without losing information.,3971
heart_histograms.png,The variable trestbps can be seen as ordinal without losing information.,3972
heart_histograms.png,The variable chol can be seen as ordinal without losing information.,3973
heart_histograms.png,The variable fbs can be seen as ordinal without losing information.,3974
heart_histograms.png,The variable restecg can be seen as ordinal without losing information.,3975
heart_histograms.png,The variable thalach can be seen as ordinal without losing information.,3976
heart_histograms.png,The variable exang can be seen as ordinal without losing information.,3977
heart_histograms.png,The variable oldpeak can be seen as ordinal without losing information.,3978
heart_histograms.png,The variable slope can be seen as ordinal without losing information.,3979
heart_histograms.png,The variable ca can be seen as ordinal without losing information.,3980
heart_histograms.png,The variable thal can be seen as ordinal without losing information.,3981
heart_histograms.png,The variable age can be seen as ordinal.,3982
heart_histograms.png,The variable sex can be seen as ordinal.,3983
heart_histograms.png,The variable cp can be seen as ordinal.,3984
heart_histograms.png,The variable trestbps can be seen as ordinal.,3985
heart_histograms.png,The variable chol can be seen as ordinal.,3986
heart_histograms.png,The variable fbs can be seen as ordinal.,3987
heart_histograms.png,The variable restecg can be seen as ordinal.,3988
heart_histograms.png,The variable thalach can be seen as ordinal.,3989
heart_histograms.png,The variable exang can be seen as ordinal.,3990
heart_histograms.png,The variable oldpeak can be seen as ordinal.,3991
heart_histograms.png,The variable slope can be seen as ordinal.,3992
heart_histograms.png,The variable ca can be seen as ordinal.,3993
heart_histograms.png,The variable thal can be seen as ordinal.,3994
heart_histograms.png,"All variables, but the class, should be dealt with as numeric.",3995
heart_histograms.png,"All variables, but the class, should be dealt with as binary.",3996
heart_histograms.png,"All variables, but the class, should be dealt with as date.",3997
heart_histograms.png,"All variables, but the class, should be dealt with as symbolic.",3998
heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 74.,3999
heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 97.,4000
heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 16.,4001
heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 49.,4002
heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 47.,4003
heart_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,4004
heart_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",4005
heart_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",4006
heart_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",4007
heart_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",4008
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",4009
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",4010
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",4011
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",4012
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 2.",4013
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 2.",4014
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 2.",4015
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 2.",4016
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 3.",4017
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 3.",4018
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 3.",4019
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 3.",4020
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 4.",4021
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 4.",4022
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 4.",4023
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 4.",4024
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 435.",4025
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 435.",4026
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 435.",4027
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 435.",4028
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 435.",4029
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 435.",4030
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 435.",4031
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 435.",4032
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 435.",4033
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 435.",4034
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 435.",4035
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 435.",4036
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 435.",4037
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 435.",4038
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 435.",4039
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 435.",4040
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 74.",4041
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 74.",4042
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 74.",4043
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 74.",4044
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 74.",4045
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 74.",4046
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 74.",4047
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 74.",4048
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 74.",4049
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 74.",4050
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 74.",4051
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 74.",4052
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 74.",4053
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 74.",4054
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 74.",4055
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 74.",4056
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 30.",4057
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 30.",4058
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 30.",4059
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 30.",4060
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 30.",4061
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 30.",4062
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 30.",4063
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 30.",4064
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 30.",4065
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 30.",4066
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 30.",4067
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 30.",4068
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 30.",4069
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 30.",4070
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 30.",4071
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 30.",4072
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 53.",4073
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 53.",4074
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 53.",4075
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 53.",4076
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 53.",4077
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 53.",4078
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 53.",4079
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 53.",4080
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 53.",4081
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 53.",4082
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 53.",4083
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 53.",4084
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 53.",4085
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 53.",4086
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 53.",4087
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 53.",4088
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A,B) as 1.",4089
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, B) as 1.",4090
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A, not B) as 1.",4091
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, not B) as 1.",4092
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A,B) as 2.",4093
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, B) as 2.",4094
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A, not B) as 2.",4095
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, not B) as 2.",4096
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A,B) as 3.",4097
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, B) as 3.",4098
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A, not B) as 3.",4099
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, not B) as 3.",4100
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A,B) as 4.",4101
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, B) as 4.",4102
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A, not B) as 4.",4103
vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, not B) as 4.",4104
vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 634 episodes.,4105
vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 788 episodes.,4106
vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 975 episodes.,4107
vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 735 episodes.,4108
vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1295 episodes.,4109
vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 122 estimators.,4110
vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 128 estimators.,4111
vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 59 estimators.,4112
vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 69 estimators.,4113
vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 78 estimators.,4114
vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 91 estimators.,4115
vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 69 estimators.,4116
vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 62 estimators.,4117
vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 131 estimators.,4118
vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 143 estimators.,4119
vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,4120
vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,4121
vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,4122
vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,4123
vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,4124
vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,4125
vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,4126
vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,4127
vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,4128
vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,4129
vehicle_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,4130
vehicle_decision_tree.png,The recall for the presented tree is higher than its accuracy.,4131
vehicle_decision_tree.png,The precision for the presented tree is higher than its accuracy.,4132
vehicle_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,4133
vehicle_decision_tree.png,The recall for the presented tree is lower than its accuracy.,4134
vehicle_decision_tree.png,The precision for the presented tree is lower than its accuracy.,4135
vehicle_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,4136
vehicle_decision_tree.png,The accuracy for the presented tree is higher than its recall.,4137
vehicle_decision_tree.png,The precision for the presented tree is higher than its recall.,4138
vehicle_decision_tree.png,The specificity for the presented tree is higher than its recall.,4139
vehicle_decision_tree.png,The accuracy for the presented tree is lower than its recall.,4140
vehicle_decision_tree.png,The precision for the presented tree is lower than its recall.,4141
vehicle_decision_tree.png,The specificity for the presented tree is lower than its recall.,4142
vehicle_decision_tree.png,The accuracy for the presented tree is higher than its precision.,4143
vehicle_decision_tree.png,The recall for the presented tree is higher than its precision.,4144
vehicle_decision_tree.png,The specificity for the presented tree is higher than its precision.,4145
vehicle_decision_tree.png,The accuracy for the presented tree is lower than its precision.,4146
vehicle_decision_tree.png,The recall for the presented tree is lower than its precision.,4147
vehicle_decision_tree.png,The specificity for the presented tree is lower than its precision.,4148
vehicle_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,4149
vehicle_decision_tree.png,The recall for the presented tree is higher than its specificity.,4150
vehicle_decision_tree.png,The precision for the presented tree is higher than its specificity.,4151
vehicle_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,4152
vehicle_decision_tree.png,The recall for the presented tree is lower than its specificity.,4153
vehicle_decision_tree.png,The precision for the presented tree is lower than its specificity.,4154
vehicle_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,4155
vehicle_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,4156
vehicle_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,4157
vehicle_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,4158
vehicle_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,4159
vehicle_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,4160
vehicle_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,4161
vehicle_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,4162
vehicle_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,4163
vehicle_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,4164
vehicle_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,4165
vehicle_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,4166
vehicle_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,4167
vehicle_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,4168
vehicle_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,4169
vehicle_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,4170
vehicle_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,4171
vehicle_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,4172
vehicle_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,4173
vehicle_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,4174
vehicle_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,4175
vehicle_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,4176
vehicle_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,4177
vehicle_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,4178
vehicle_decision_tree.png,The number of True Positives reported in the same tree is 34.,4179
vehicle_decision_tree.png,The number of False Positives reported in the same tree is 19.,4180
vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 21.,4181
vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 15.,4182
vehicle_decision_tree.png,The number of True Positives reported in the same tree is 44.,4183
vehicle_decision_tree.png,The number of False Positives reported in the same tree is 24.,4184
vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 30.,4185
vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 38.,4186
vehicle_decision_tree.png,The number of True Positives reported in the same tree is 20.,4187
vehicle_decision_tree.png,The number of False Positives reported in the same tree is 31.,4188
vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 50.,4189
vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 49.,4190
vehicle_decision_tree.png,The number of True Positives reported in the same tree is 43.,4191
vehicle_decision_tree.png,The number of False Positives reported in the same tree is 46.,4192
vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 33.,4193
vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 28.,4194
vehicle_decision_tree.png,The number of True Positives reported in the same tree is 12.,4195
vehicle_decision_tree.png,The number of False Positives reported in the same tree is 35.,4196
vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 26.,4197
vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 32.,4198
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,4199
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,4200
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,4201
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,4202
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,4203
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,4204
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,4205
vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,4206
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,4207
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,4208
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,4209
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,4210
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,4211
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,4212
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,4213
vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,4214
vehicle_decision_tree.png,The accuracy for the presented tree is higher than 80%.,4215
vehicle_decision_tree.png,The recall for the presented tree is higher than 85%.,4216
vehicle_decision_tree.png,The precision for the presented tree is higher than 67%.,4217
vehicle_decision_tree.png,The specificity for the presented tree is higher than 77%.,4218
vehicle_decision_tree.png,The accuracy for the presented tree is lower than 78%.,4219
vehicle_decision_tree.png,The recall for the presented tree is lower than 89%.,4220
vehicle_decision_tree.png,The precision for the presented tree is lower than 87%.,4221
vehicle_decision_tree.png,The specificity for the presented tree is lower than 88%.,4222
vehicle_decision_tree.png,The accuracy for the presented tree is higher than 90%.,4223
vehicle_decision_tree.png,The recall for the presented tree is higher than 76%.,4224
vehicle_decision_tree.png,The precision for the presented tree is higher than 72%.,4225
vehicle_decision_tree.png,The specificity for the presented tree is higher than 60%.,4226
vehicle_decision_tree.png,The accuracy for the presented tree is lower than 81%.,4227
vehicle_decision_tree.png,The recall for the presented tree is lower than 86%.,4228
vehicle_decision_tree.png,The precision for the presented tree is lower than 73%.,4229
vehicle_decision_tree.png,The specificity for the presented tree is lower than 68%.,4230
vehicle_decision_tree.png,The accuracy for the presented tree is higher than 64%.,4231
vehicle_decision_tree.png,The recall for the presented tree is higher than 66%.,4232
vehicle_decision_tree.png,The precision for the presented tree is higher than 63%.,4233
vehicle_decision_tree.png,The specificity for the presented tree is higher than 75%.,4234
vehicle_decision_tree.png,The accuracy for the presented tree is lower than 74%.,4235
vehicle_decision_tree.png,The recall for the presented tree is lower than 70%.,4236
vehicle_decision_tree.png,The precision for the presented tree is lower than 83%.,4237
vehicle_decision_tree.png,The specificity for the presented tree is lower than 69%.,4238
vehicle_decision_tree.png,The accuracy for the presented tree is higher than 84%.,4239
vehicle_decision_tree.png,The recall for the presented tree is higher than 79%.,4240
vehicle_decision_tree.png,The precision for the presented tree is higher than 71%.,4241
vehicle_decision_tree.png,The specificity for the presented tree is higher than 82%.,4242
vehicle_decision_tree.png,The accuracy for the presented tree is lower than 61%.,4243
vehicle_decision_tree.png,The recall for the presented tree is lower than 65%.,4244
vehicle_decision_tree.png,The precision for the presented tree is lower than 62%.,4245
vehicle_decision_tree.png,The specificity for the presented tree is lower than 85%.,4246
vehicle_decision_tree.png,The accuracy for the presented tree is higher than 88%.,4247
vehicle_decision_tree.png,The recall for the presented tree is higher than 70%.,4248
vehicle_decision_tree.png,The precision for the presented tree is higher than 77%.,4249
vehicle_decision_tree.png,The specificity for the presented tree is higher than 77%.,4250
vehicle_decision_tree.png,The accuracy for the presented tree is lower than 63%.,4251
vehicle_decision_tree.png,The recall for the presented tree is lower than 69%.,4252
vehicle_decision_tree.png,The precision for the presented tree is lower than 87%.,4253
vehicle_decision_tree.png,The specificity for the presented tree is lower than 85%.,4254
vehicle_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",4255
vehicle_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",4256
vehicle_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",4257
vehicle_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",4258
vehicle_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",4259
vehicle_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",4260
vehicle_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,4261
vehicle_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,4262
vehicle_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,4263
vehicle_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,4264
vehicle_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,4265
vehicle_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,4266
vehicle_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,4267
vehicle_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,4268
vehicle_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,4269
vehicle_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,4270
vehicle_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,4271
vehicle_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,4272
vehicle_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,4273
vehicle_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,4274
vehicle_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,4275
vehicle_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,4276
vehicle_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,4277
vehicle_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,4278
vehicle_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,4279
vehicle_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,4280
vehicle_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,4281
vehicle_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,4282
vehicle_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,4283
vehicle_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,4284
vehicle_overfitting_knn.png,KNN is in overfitting for k less than 2.,4285
vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 2.,4286
vehicle_overfitting_knn.png,KNN is in overfitting for k less than 3.,4287
vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 3.,4288
vehicle_overfitting_knn.png,KNN is in overfitting for k less than 4.,4289
vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 4.,4290
vehicle_overfitting_knn.png,KNN is in overfitting for k less than 5.,4291
vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 5.,4292
vehicle_overfitting_knn.png,KNN is in overfitting for k less than 6.,4293
vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 6.,4294
vehicle_overfitting_knn.png,KNN is in overfitting for k less than 7.,4295
vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 7.,4296
vehicle_overfitting_knn.png,KNN is in overfitting for k less than 8.,4297
vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 8.,4298
vehicle_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",4299
vehicle_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",4300
vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",4301
vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",4302
vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",4303
vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",4304
vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",4305
vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",4306
vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",4307
vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",4308
vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",4309
vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",4310
vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",4311
vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",4312
vehicle_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,4313
vehicle_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,4314
vehicle_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,4315
vehicle_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,4316
vehicle_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,4317
vehicle_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,4318
vehicle_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,4319
vehicle_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,4320
vehicle_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,4321
vehicle_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,4322
vehicle_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,4323
vehicle_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,4324
vehicle_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,4325
vehicle_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,4326
vehicle_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,4327
vehicle_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,4328
vehicle_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,4329
vehicle_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,4330
vehicle_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,4331
vehicle_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,4332
vehicle_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,4333
vehicle_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,4334
vehicle_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,4335
vehicle_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,4336
vehicle_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,4337
vehicle_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,4338
vehicle_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,4339
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable COMPACTNESS.,4340
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable COMPACTNESS.,4341
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable COMPACTNESS.,4342
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable COMPACTNESS.,4343
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable COMPACTNESS.,4344
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable COMPACTNESS.,4345
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable COMPACTNESS.,4346
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable COMPACTNESS.,4347
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable COMPACTNESS.,4348
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable COMPACTNESS.,4349
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable COMPACTNESS.,4350
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable COMPACTNESS.,4351
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable COMPACTNESS.,4352
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable COMPACTNESS.,4353
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable COMPACTNESS.,4354
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable COMPACTNESS.,4355
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable COMPACTNESS.,4356
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable COMPACTNESS.,4357
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable CIRCULARITY.,4358
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable CIRCULARITY.,4359
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable CIRCULARITY.,4360
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable CIRCULARITY.,4361
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable CIRCULARITY.,4362
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable CIRCULARITY.,4363
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable CIRCULARITY.,4364
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable CIRCULARITY.,4365
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable CIRCULARITY.,4366
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable CIRCULARITY.,4367
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable CIRCULARITY.,4368
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable CIRCULARITY.,4369
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable CIRCULARITY.,4370
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable CIRCULARITY.,4371
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable CIRCULARITY.,4372
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable CIRCULARITY.,4373
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable CIRCULARITY.,4374
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable DISTANCE CIRCULARITY.,4375
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable DISTANCE CIRCULARITY.,4376
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable DISTANCE CIRCULARITY.,4377
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable DISTANCE CIRCULARITY.,4378
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable DISTANCE CIRCULARITY.,4379
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable DISTANCE CIRCULARITY.,4380
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable DISTANCE CIRCULARITY.,4381
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable DISTANCE CIRCULARITY.,4382
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable DISTANCE CIRCULARITY.,4383
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable DISTANCE CIRCULARITY.,4384
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable DISTANCE CIRCULARITY.,4385
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable DISTANCE CIRCULARITY.,4386
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable DISTANCE CIRCULARITY.,4387
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable DISTANCE CIRCULARITY.,4388
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable DISTANCE CIRCULARITY.,4389
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable DISTANCE CIRCULARITY.,4390
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable DISTANCE CIRCULARITY.,4391
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable DISTANCE CIRCULARITY.,4392
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable RADIUS RATIO.,4393
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable RADIUS RATIO.,4394
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable RADIUS RATIO.,4395
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable RADIUS RATIO.,4396
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable RADIUS RATIO.,4397
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable RADIUS RATIO.,4398
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable RADIUS RATIO.,4399
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable RADIUS RATIO.,4400
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable RADIUS RATIO.,4401
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable RADIUS RATIO.,4402
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable RADIUS RATIO.,4403
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable RADIUS RATIO.,4404
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable RADIUS RATIO.,4405
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable RADIUS RATIO.,4406
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable RADIUS RATIO.,4407
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable RADIUS RATIO.,4408
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable RADIUS RATIO.,4409
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable RADIUS RATIO.,4410
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable PR AXIS ASPECT RATIO.,4411
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable PR AXIS ASPECT RATIO.,4412
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable PR AXIS ASPECT RATIO.,4413
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable PR AXIS ASPECT RATIO.,4414
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable PR AXIS ASPECT RATIO.,4415
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable PR AXIS ASPECT RATIO.,4416
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable PR AXIS ASPECT RATIO.,4417
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable PR AXIS ASPECT RATIO.,4418
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable PR AXIS ASPECT RATIO.,4419
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable PR AXIS ASPECT RATIO.,4420
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable PR AXIS ASPECT RATIO.,4421
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable PR AXIS ASPECT RATIO.,4422
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable PR AXIS ASPECT RATIO.,4423
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable PR AXIS ASPECT RATIO.,4424
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable PR AXIS ASPECT RATIO.,4425
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable PR AXIS ASPECT RATIO.,4426
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable PR AXIS ASPECT RATIO.,4427
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable PR AXIS ASPECT RATIO.,4428
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MAX LENGTH ASPECT RATIO.,4429
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MAX LENGTH ASPECT RATIO.,4430
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MAX LENGTH ASPECT RATIO.,4431
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable MAX LENGTH ASPECT RATIO.,4432
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MAX LENGTH ASPECT RATIO.,4433
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAX LENGTH ASPECT RATIO.,4434
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MAX LENGTH ASPECT RATIO.,4435
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MAX LENGTH ASPECT RATIO.,4436
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MAX LENGTH ASPECT RATIO.,4437
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable MAX LENGTH ASPECT RATIO.,4438
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MAX LENGTH ASPECT RATIO.,4439
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MAX LENGTH ASPECT RATIO.,4440
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MAX LENGTH ASPECT RATIO.,4441
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MAX LENGTH ASPECT RATIO.,4442
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable MAX LENGTH ASPECT RATIO.,4443
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable MAX LENGTH ASPECT RATIO.,4444
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MAX LENGTH ASPECT RATIO.,4445
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MAX LENGTH ASPECT RATIO.,4446
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable SCATTER RATIO.,4447
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable SCATTER RATIO.,4448
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable SCATTER RATIO.,4449
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable SCATTER RATIO.,4450
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable SCATTER RATIO.,4451
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable SCATTER RATIO.,4452
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable SCATTER RATIO.,4453
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable SCATTER RATIO.,4454
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable SCATTER RATIO.,4455
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable SCATTER RATIO.,4456
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable SCATTER RATIO.,4457
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable SCATTER RATIO.,4458
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable SCATTER RATIO.,4459
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable SCATTER RATIO.,4460
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable SCATTER RATIO.,4461
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable SCATTER RATIO.,4462
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable SCATTER RATIO.,4463
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable SCATTER RATIO.,4464
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable ELONGATEDNESS.,4465
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable ELONGATEDNESS.,4466
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable ELONGATEDNESS.,4467
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable ELONGATEDNESS.,4468
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable ELONGATEDNESS.,4469
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable ELONGATEDNESS.,4470
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable ELONGATEDNESS.,4471
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable ELONGATEDNESS.,4472
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable ELONGATEDNESS.,4473
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable ELONGATEDNESS.,4474
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable ELONGATEDNESS.,4475
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable ELONGATEDNESS.,4476
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable ELONGATEDNESS.,4477
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable ELONGATEDNESS.,4478
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable ELONGATEDNESS.,4479
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable ELONGATEDNESS.,4480
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable ELONGATEDNESS.,4481
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable ELONGATEDNESS.,4482
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable PR AXISRECTANGULAR.,4483
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable PR AXISRECTANGULAR.,4484
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable PR AXISRECTANGULAR.,4485
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable PR AXISRECTANGULAR.,4486
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable PR AXISRECTANGULAR.,4487
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable PR AXISRECTANGULAR.,4488
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable PR AXISRECTANGULAR.,4489
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable PR AXISRECTANGULAR.,4490
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable PR AXISRECTANGULAR.,4491
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable PR AXISRECTANGULAR.,4492
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable PR AXISRECTANGULAR.,4493
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable PR AXISRECTANGULAR.,4494
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable PR AXISRECTANGULAR.,4495
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable PR AXISRECTANGULAR.,4496
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable PR AXISRECTANGULAR.,4497
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable PR AXISRECTANGULAR.,4498
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable PR AXISRECTANGULAR.,4499
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable PR AXISRECTANGULAR.,4500
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable LENGTHRECTANGULAR.,4501
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable LENGTHRECTANGULAR.,4502
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable LENGTHRECTANGULAR.,4503
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable LENGTHRECTANGULAR.,4504
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable LENGTHRECTANGULAR.,4505
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable LENGTHRECTANGULAR.,4506
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable LENGTHRECTANGULAR.,4507
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable LENGTHRECTANGULAR.,4508
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable LENGTHRECTANGULAR.,4509
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable LENGTHRECTANGULAR.,4510
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable LENGTHRECTANGULAR.,4511
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable LENGTHRECTANGULAR.,4512
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable LENGTHRECTANGULAR.,4513
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable LENGTHRECTANGULAR.,4514
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable LENGTHRECTANGULAR.,4515
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable LENGTHRECTANGULAR.,4516
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable LENGTHRECTANGULAR.,4517
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable LENGTHRECTANGULAR.,4518
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MAJORVARIANCE.,4519
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MAJORVARIANCE.,4520
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MAJORVARIANCE.,4521
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable MAJORVARIANCE.,4522
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MAJORVARIANCE.,4523
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable MAJORVARIANCE.,4524
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAJORVARIANCE.,4525
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MAJORVARIANCE.,4526
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MAJORVARIANCE.,4527
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MAJORVARIANCE.,4528
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MAJORVARIANCE.,4529
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MAJORVARIANCE.,4530
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MAJORVARIANCE.,4531
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MAJORVARIANCE.,4532
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable MAJORVARIANCE.,4533
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable MAJORVARIANCE.,4534
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MAJORVARIANCE.,4535
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MAJORVARIANCE.,4536
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MINORVARIANCE.,4537
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MINORVARIANCE.,4538
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MINORVARIANCE.,4539
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable MINORVARIANCE.,4540
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MINORVARIANCE.,4541
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable MINORVARIANCE.,4542
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MINORVARIANCE.,4543
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MINORVARIANCE.,4544
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MINORVARIANCE.,4545
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MINORVARIANCE.,4546
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable MINORVARIANCE.,4547
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MINORVARIANCE.,4548
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MINORVARIANCE.,4549
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MINORVARIANCE.,4550
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable MINORVARIANCE.,4551
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable MINORVARIANCE.,4552
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MINORVARIANCE.,4553
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MINORVARIANCE.,4554
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable GYRATIONRADIUS.,4555
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable GYRATIONRADIUS.,4556
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable GYRATIONRADIUS.,4557
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable GYRATIONRADIUS.,4558
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable GYRATIONRADIUS.,4559
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable GYRATIONRADIUS.,4560
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable GYRATIONRADIUS.,4561
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable GYRATIONRADIUS.,4562
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable GYRATIONRADIUS.,4563
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable GYRATIONRADIUS.,4564
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable GYRATIONRADIUS.,4565
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable GYRATIONRADIUS.,4566
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable GYRATIONRADIUS.,4567
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable GYRATIONRADIUS.,4568
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable GYRATIONRADIUS.,4569
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable GYRATIONRADIUS.,4570
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable GYRATIONRADIUS.,4571
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable GYRATIONRADIUS.,4572
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MAJORSKEWNESS.,4573
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MAJORSKEWNESS.,4574
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MAJORSKEWNESS.,4575
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable MAJORSKEWNESS.,4576
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MAJORSKEWNESS.,4577
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable MAJORSKEWNESS.,4578
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAJORSKEWNESS.,4579
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MAJORSKEWNESS.,4580
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MAJORSKEWNESS.,4581
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MAJORSKEWNESS.,4582
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable MAJORSKEWNESS.,4583
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MAJORSKEWNESS.,4584
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MAJORSKEWNESS.,4585
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MAJORSKEWNESS.,4586
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable MAJORSKEWNESS.,4587
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable MAJORSKEWNESS.,4588
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MAJORSKEWNESS.,4589
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MAJORSKEWNESS.,4590
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MINORSKEWNESS.,4591
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MINORSKEWNESS.,4592
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MINORSKEWNESS.,4593
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable MINORSKEWNESS.,4594
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MINORSKEWNESS.,4595
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable MINORSKEWNESS.,4596
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MINORSKEWNESS.,4597
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MINORSKEWNESS.,4598
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MINORSKEWNESS.,4599
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MINORSKEWNESS.,4600
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable MINORSKEWNESS.,4601
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MINORSKEWNESS.,4602
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MINORSKEWNESS.,4603
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MINORSKEWNESS.,4604
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable MINORSKEWNESS.,4605
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable MINORSKEWNESS.,4606
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MINORSKEWNESS.,4607
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MINORSKEWNESS.,4608
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MINORKURTOSIS.,4609
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MINORKURTOSIS.,4610
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MINORKURTOSIS.,4611
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable MINORKURTOSIS.,4612
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MINORKURTOSIS.,4613
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable MINORKURTOSIS.,4614
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MINORKURTOSIS.,4615
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MINORKURTOSIS.,4616
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MINORKURTOSIS.,4617
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MINORKURTOSIS.,4618
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable MINORKURTOSIS.,4619
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MINORKURTOSIS.,4620
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MINORKURTOSIS.,4621
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MINORKURTOSIS.,4622
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MINORKURTOSIS.,4623
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable MINORKURTOSIS.,4624
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MINORKURTOSIS.,4625
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MINORKURTOSIS.,4626
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MAJORKURTOSIS.,4627
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MAJORKURTOSIS.,4628
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MAJORKURTOSIS.,4629
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable MAJORKURTOSIS.,4630
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MAJORKURTOSIS.,4631
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable MAJORKURTOSIS.,4632
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAJORKURTOSIS.,4633
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MAJORKURTOSIS.,4634
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MAJORKURTOSIS.,4635
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MAJORKURTOSIS.,4636
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable MAJORKURTOSIS.,4637
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MAJORKURTOSIS.,4638
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MAJORKURTOSIS.,4639
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MAJORKURTOSIS.,4640
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MAJORKURTOSIS.,4641
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable MAJORKURTOSIS.,4642
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MAJORKURTOSIS.,4643
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MAJORKURTOSIS.,4644
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable HOLLOWS RATIO.,4645
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable HOLLOWS RATIO.,4646
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable HOLLOWS RATIO.,4647
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable HOLLOWS RATIO.,4648
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable HOLLOWS RATIO.,4649
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable HOLLOWS RATIO.,4650
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable HOLLOWS RATIO.,4651
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable HOLLOWS RATIO.,4652
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable HOLLOWS RATIO.,4653
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable HOLLOWS RATIO.,4654
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable HOLLOWS RATIO.,4655
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable HOLLOWS RATIO.,4656
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable HOLLOWS RATIO.,4657
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable HOLLOWS RATIO.,4658
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable HOLLOWS RATIO.,4659
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable HOLLOWS RATIO.,4660
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable HOLLOWS RATIO.,4661
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable HOLLOWS RATIO.,4662
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable target.,4663
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable target.,4664
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable target.,4665
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable target.,4666
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable target.,4667
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable target.,4668
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable target.,4669
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable target.,4670
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable target.,4671
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable target.,4672
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable target.,4673
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable target.,4674
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable target.,4675
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable target.,4676
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable target.,4677
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable target.,4678
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable target.,4679
vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable target.,4680
vehicle_pca.png,The first 2 principal components are enough for explaining half the data variance.,4681
vehicle_pca.png,The first 3 principal components are enough for explaining half the data variance.,4682
vehicle_pca.png,The first 4 principal components are enough for explaining half the data variance.,4683
vehicle_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,4684
vehicle_correlation_heatmap.png,Removing variable COMPACTNESS might improve the training of decision trees .,4685
vehicle_correlation_heatmap.png,Removing variable CIRCULARITY might improve the training of decision trees .,4686
vehicle_correlation_heatmap.png,Removing variable DISTANCE CIRCULARITY might improve the training of decision trees .,4687
vehicle_correlation_heatmap.png,Removing variable RADIUS RATIO might improve the training of decision trees .,4688
vehicle_correlation_heatmap.png,Removing variable PR AXIS ASPECT RATIO might improve the training of decision trees .,4689
vehicle_correlation_heatmap.png,Removing variable MAX LENGTH ASPECT RATIO might improve the training of decision trees .,4690
vehicle_correlation_heatmap.png,Removing variable SCATTER RATIO might improve the training of decision trees .,4691
vehicle_correlation_heatmap.png,Removing variable ELONGATEDNESS might improve the training of decision trees .,4692
vehicle_correlation_heatmap.png,Removing variable PR AXISRECTANGULAR might improve the training of decision trees .,4693
vehicle_correlation_heatmap.png,Removing variable LENGTHRECTANGULAR might improve the training of decision trees .,4694
vehicle_correlation_heatmap.png,Removing variable MAJORVARIANCE might improve the training of decision trees .,4695
vehicle_correlation_heatmap.png,Removing variable MINORVARIANCE might improve the training of decision trees .,4696
vehicle_correlation_heatmap.png,Removing variable GYRATIONRADIUS might improve the training of decision trees .,4697
vehicle_correlation_heatmap.png,Removing variable MAJORSKEWNESS might improve the training of decision trees .,4698
vehicle_correlation_heatmap.png,Removing variable MINORSKEWNESS might improve the training of decision trees .,4699
vehicle_correlation_heatmap.png,Removing variable MINORKURTOSIS might improve the training of decision trees .,4700
vehicle_correlation_heatmap.png,Removing variable MAJORKURTOSIS might improve the training of decision trees .,4701
vehicle_correlation_heatmap.png,Removing variable HOLLOWS RATIO might improve the training of decision trees .,4702
vehicle_correlation_heatmap.png,Removing variable target might improve the training of decision trees .,4703
vehicle_histograms.png,"Not knowing the semantics of COMPACTNESS variable, dummification could have been a more adequate codification.",4704
vehicle_histograms.png,"Not knowing the semantics of CIRCULARITY variable, dummification could have been a more adequate codification.",4705
vehicle_histograms.png,"Not knowing the semantics of DISTANCE CIRCULARITY variable, dummification could have been a more adequate codification.",4706
vehicle_histograms.png,"Not knowing the semantics of RADIUS RATIO variable, dummification could have been a more adequate codification.",4707
vehicle_histograms.png,"Not knowing the semantics of PR AXIS ASPECT RATIO variable, dummification could have been a more adequate codification.",4708
vehicle_histograms.png,"Not knowing the semantics of MAX LENGTH ASPECT RATIO variable, dummification could have been a more adequate codification.",4709
vehicle_histograms.png,"Not knowing the semantics of SCATTER RATIO variable, dummification could have been a more adequate codification.",4710
vehicle_histograms.png,"Not knowing the semantics of ELONGATEDNESS variable, dummification could have been a more adequate codification.",4711
vehicle_histograms.png,"Not knowing the semantics of PR AXISRECTANGULAR variable, dummification could have been a more adequate codification.",4712
vehicle_histograms.png,"Not knowing the semantics of LENGTHRECTANGULAR variable, dummification could have been a more adequate codification.",4713
vehicle_histograms.png,"Not knowing the semantics of MAJORVARIANCE variable, dummification could have been a more adequate codification.",4714
vehicle_histograms.png,"Not knowing the semantics of MINORVARIANCE variable, dummification could have been a more adequate codification.",4715
vehicle_histograms.png,"Not knowing the semantics of GYRATIONRADIUS variable, dummification could have been a more adequate codification.",4716
vehicle_histograms.png,"Not knowing the semantics of MAJORSKEWNESS variable, dummification could have been a more adequate codification.",4717
vehicle_histograms.png,"Not knowing the semantics of MINORSKEWNESS variable, dummification could have been a more adequate codification.",4718
vehicle_histograms.png,"Not knowing the semantics of MINORKURTOSIS variable, dummification could have been a more adequate codification.",4719
vehicle_histograms.png,"Not knowing the semantics of MAJORKURTOSIS variable, dummification could have been a more adequate codification.",4720
vehicle_histograms.png,"Not knowing the semantics of HOLLOWS RATIO variable, dummification could have been a more adequate codification.",4721
vehicle_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,4722
vehicle_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",4723
vehicle_histograms.png,"Given the usual semantics of COMPACTNESS variable, dummification would have been a better codification.",4724
vehicle_histograms.png,"Given the usual semantics of CIRCULARITY variable, dummification would have been a better codification.",4725
vehicle_histograms.png,"Given the usual semantics of DISTANCE CIRCULARITY variable, dummification would have been a better codification.",4726
vehicle_histograms.png,"Given the usual semantics of RADIUS RATIO variable, dummification would have been a better codification.",4727
vehicle_histograms.png,"Given the usual semantics of PR AXIS ASPECT RATIO variable, dummification would have been a better codification.",4728
vehicle_histograms.png,"Given the usual semantics of MAX LENGTH ASPECT RATIO variable, dummification would have been a better codification.",4729
vehicle_histograms.png,"Given the usual semantics of SCATTER RATIO variable, dummification would have been a better codification.",4730
vehicle_histograms.png,"Given the usual semantics of ELONGATEDNESS variable, dummification would have been a better codification.",4731
vehicle_histograms.png,"Given the usual semantics of PR AXISRECTANGULAR variable, dummification would have been a better codification.",4732
vehicle_histograms.png,"Given the usual semantics of LENGTHRECTANGULAR variable, dummification would have been a better codification.",4733
vehicle_histograms.png,"Given the usual semantics of MAJORVARIANCE variable, dummification would have been a better codification.",4734
vehicle_histograms.png,"Given the usual semantics of MINORVARIANCE variable, dummification would have been a better codification.",4735
vehicle_histograms.png,"Given the usual semantics of GYRATIONRADIUS variable, dummification would have been a better codification.",4736
vehicle_histograms.png,"Given the usual semantics of MAJORSKEWNESS variable, dummification would have been a better codification.",4737
vehicle_histograms.png,"Given the usual semantics of MINORSKEWNESS variable, dummification would have been a better codification.",4738
vehicle_histograms.png,"Given the usual semantics of MINORKURTOSIS variable, dummification would have been a better codification.",4739
vehicle_histograms.png,"Given the usual semantics of MAJORKURTOSIS variable, dummification would have been a better codification.",4740
vehicle_histograms.png,"Given the usual semantics of HOLLOWS RATIO variable, dummification would have been a better codification.",4741
vehicle_histograms.png,The variable COMPACTNESS can be coded as ordinal without losing information.,4742
vehicle_histograms.png,The variable CIRCULARITY can be coded as ordinal without losing information.,4743
vehicle_histograms.png,The variable DISTANCE CIRCULARITY can be coded as ordinal without losing information.,4744
vehicle_histograms.png,The variable RADIUS RATIO can be coded as ordinal without losing information.,4745
vehicle_histograms.png,The variable PR AXIS ASPECT RATIO can be coded as ordinal without losing information.,4746
vehicle_histograms.png,The variable MAX LENGTH ASPECT RATIO can be coded as ordinal without losing information.,4747
vehicle_histograms.png,The variable SCATTER RATIO can be coded as ordinal without losing information.,4748
vehicle_histograms.png,The variable ELONGATEDNESS can be coded as ordinal without losing information.,4749
vehicle_histograms.png,The variable PR AXISRECTANGULAR can be coded as ordinal without losing information.,4750
vehicle_histograms.png,The variable LENGTHRECTANGULAR can be coded as ordinal without losing information.,4751
vehicle_histograms.png,The variable MAJORVARIANCE can be coded as ordinal without losing information.,4752
vehicle_histograms.png,The variable MINORVARIANCE can be coded as ordinal without losing information.,4753
vehicle_histograms.png,The variable GYRATIONRADIUS can be coded as ordinal without losing information.,4754
vehicle_histograms.png,The variable MAJORSKEWNESS can be coded as ordinal without losing information.,4755
vehicle_histograms.png,The variable MINORSKEWNESS can be coded as ordinal without losing information.,4756
vehicle_histograms.png,The variable MINORKURTOSIS can be coded as ordinal without losing information.,4757
vehicle_histograms.png,The variable MAJORKURTOSIS can be coded as ordinal without losing information.,4758
vehicle_histograms.png,The variable HOLLOWS RATIO can be coded as ordinal without losing information.,4759
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS variable, dummification would be the most adequate encoding.",4760
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY variable, dummification would be the most adequate encoding.",4761
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY variable, dummification would be the most adequate encoding.",4762
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO variable, dummification would be the most adequate encoding.",4763
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO variable, dummification would be the most adequate encoding.",4764
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO variable, dummification would be the most adequate encoding.",4765
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO variable, dummification would be the most adequate encoding.",4766
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS variable, dummification would be the most adequate encoding.",4767
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR variable, dummification would be the most adequate encoding.",4768
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR variable, dummification would be the most adequate encoding.",4769
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE variable, dummification would be the most adequate encoding.",4770
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE variable, dummification would be the most adequate encoding.",4771
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS variable, dummification would be the most adequate encoding.",4772
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS variable, dummification would be the most adequate encoding.",4773
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS variable, dummification would be the most adequate encoding.",4774
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS variable, dummification would be the most adequate encoding.",4775
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS variable, dummification would be the most adequate encoding.",4776
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO variable, dummification would be the most adequate encoding.",4777
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4778
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4779
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4780
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4781
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4782
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4783
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4784
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4785
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4786
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4787
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4788
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4789
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4790
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4791
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4792
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4793
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4794
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4795
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4796
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4797
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4798
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4799
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4800
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4801
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4802
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4803
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4804
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4805
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4806
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4807
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4808
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4809
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4810
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4811
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4812
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4813
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4814
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4815
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4816
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4817
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4818
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4819
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4820
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4821
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4822
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4823
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4824
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4825
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4826
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4827
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4828
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4829
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4830
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4831
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4832
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4833
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4834
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4835
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4836
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4837
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4838
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4839
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4840
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4841
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4842
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4843
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4844
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4845
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4846
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4847
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4848
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4849
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4850
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4851
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4852
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4853
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4854
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4855
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4856
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4857
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4858
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4859
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4860
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4861
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4862
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4863
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4864
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4865
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4866
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4867
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4868
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4869
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4870
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4871
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4872
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4873
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4874
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4875
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4876
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4877
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4878
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4879
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4880
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4881
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4882
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4883
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4884
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4885
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4886
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4887
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4888
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4889
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4890
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4891
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4892
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4893
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4894
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4895
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4896
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4897
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4898
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4899
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4900
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4901
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4902
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4903
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4904
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4905
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4906
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4907
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4908
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4909
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4910
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4911
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4912
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4913
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4914
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4915
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4916
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4917
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4918
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4919
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4920
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4921
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4922
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4923
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4924
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4925
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4926
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4927
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4928
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4929
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4930
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4931
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4932
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4933
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4934
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4935
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4936
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4937
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4938
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4939
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4940
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4941
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4942
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4943
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4944
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4945
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4946
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4947
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4948
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4949
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4950
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4951
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4952
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4953
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4954
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4955
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4956
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4957
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4958
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4959
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4960
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4961
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4962
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4963
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4964
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4965
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4966
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4967
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4968
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4969
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4970
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4971
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4972
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4973
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4974
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4975
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4976
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4977
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4978
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4979
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4980
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4981
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4982
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4983
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4984
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4985
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4986
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4987
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4988
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4989
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4990
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4991
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4992
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4993
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4994
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4995
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4996
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4997
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4998
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4999
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5000
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5001
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5002
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5003
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5004
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5005
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5006
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5007
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5008
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5009
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5010
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5011
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5012
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5013
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5014
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5015
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5016
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5017
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5018
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5019
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5020
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5021
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5022
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5023
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5024
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5025
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5026
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5027
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5028
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5029
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5030
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5031
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5032
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5033
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5034
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5035
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5036
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5037
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5038
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5039
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5040
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5041
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5042
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5043
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5044
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5045
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5046
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5047
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5048
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5049
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5050
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5051
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5052
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5053
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5054
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5055
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5056
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5057
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5058
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5059
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5060
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5061
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5062
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5063
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5064
vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5065
vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5066
vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5067
vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5068
vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5069
vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5070
vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5071
vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5072
vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5073
vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5074
vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5075
vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5076
vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5077
vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5078
vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5079
vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5080
vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5081
vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5082
vehicle_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,5083
vehicle_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,5084
vehicle_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,5085
vehicle_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",5086
vehicle_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",5087
vehicle_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",5088
vehicle_correlation_heatmap.png,Variables CIRCULARITY and COMPACTNESS seem to be useful for classification tasks.,5089
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and COMPACTNESS seem to be useful for classification tasks.,5090
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and COMPACTNESS seem to be useful for classification tasks.,5091
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and COMPACTNESS seem to be useful for classification tasks.,5092
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and COMPACTNESS seem to be useful for classification tasks.,5093
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and COMPACTNESS seem to be useful for classification tasks.,5094
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and COMPACTNESS seem to be useful for classification tasks.,5095
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and COMPACTNESS seem to be useful for classification tasks.,5096
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and COMPACTNESS seem to be useful for classification tasks.,5097
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and COMPACTNESS seem to be useful for classification tasks.,5098
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and COMPACTNESS seem to be useful for classification tasks.,5099
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and COMPACTNESS seem to be useful for classification tasks.,5100
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and COMPACTNESS seem to be useful for classification tasks.,5101
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and COMPACTNESS seem to be useful for classification tasks.,5102
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and COMPACTNESS seem to be useful for classification tasks.,5103
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and COMPACTNESS seem to be useful for classification tasks.,5104
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and COMPACTNESS seem to be useful for classification tasks.,5105
vehicle_correlation_heatmap.png,Variables target and COMPACTNESS seem to be useful for classification tasks.,5106
vehicle_correlation_heatmap.png,Variables COMPACTNESS and CIRCULARITY seem to be useful for classification tasks.,5107
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and CIRCULARITY seem to be useful for classification tasks.,5108
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and CIRCULARITY seem to be useful for classification tasks.,5109
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and CIRCULARITY seem to be useful for classification tasks.,5110
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and CIRCULARITY seem to be useful for classification tasks.,5111
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and CIRCULARITY seem to be useful for classification tasks.,5112
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and CIRCULARITY seem to be useful for classification tasks.,5113
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and CIRCULARITY seem to be useful for classification tasks.,5114
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and CIRCULARITY seem to be useful for classification tasks.,5115
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and CIRCULARITY seem to be useful for classification tasks.,5116
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and CIRCULARITY seem to be useful for classification tasks.,5117
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and CIRCULARITY seem to be useful for classification tasks.,5118
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and CIRCULARITY seem to be useful for classification tasks.,5119
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and CIRCULARITY seem to be useful for classification tasks.,5120
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and CIRCULARITY seem to be useful for classification tasks.,5121
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and CIRCULARITY seem to be useful for classification tasks.,5122
vehicle_correlation_heatmap.png,Variables target and CIRCULARITY seem to be useful for classification tasks.,5123
vehicle_correlation_heatmap.png,Variables COMPACTNESS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5124
vehicle_correlation_heatmap.png,Variables CIRCULARITY and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5125
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5126
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5127
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5128
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5129
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5130
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5131
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5132
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5133
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5134
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5135
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5136
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5137
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5138
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5139
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5140
vehicle_correlation_heatmap.png,Variables target and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5141
vehicle_correlation_heatmap.png,Variables COMPACTNESS and RADIUS RATIO seem to be useful for classification tasks.,5142
vehicle_correlation_heatmap.png,Variables CIRCULARITY and RADIUS RATIO seem to be useful for classification tasks.,5143
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and RADIUS RATIO seem to be useful for classification tasks.,5144
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and RADIUS RATIO seem to be useful for classification tasks.,5145
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and RADIUS RATIO seem to be useful for classification tasks.,5146
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and RADIUS RATIO seem to be useful for classification tasks.,5147
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and RADIUS RATIO seem to be useful for classification tasks.,5148
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and RADIUS RATIO seem to be useful for classification tasks.,5149
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and RADIUS RATIO seem to be useful for classification tasks.,5150
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and RADIUS RATIO seem to be useful for classification tasks.,5151
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and RADIUS RATIO seem to be useful for classification tasks.,5152
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and RADIUS RATIO seem to be useful for classification tasks.,5153
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and RADIUS RATIO seem to be useful for classification tasks.,5154
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and RADIUS RATIO seem to be useful for classification tasks.,5155
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and RADIUS RATIO seem to be useful for classification tasks.,5156
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and RADIUS RATIO seem to be useful for classification tasks.,5157
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and RADIUS RATIO seem to be useful for classification tasks.,5158
vehicle_correlation_heatmap.png,Variables target and RADIUS RATIO seem to be useful for classification tasks.,5159
vehicle_correlation_heatmap.png,Variables COMPACTNESS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5160
vehicle_correlation_heatmap.png,Variables CIRCULARITY and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5161
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5162
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5163
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5164
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5165
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5166
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5167
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5168
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5169
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5170
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5171
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5172
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5173
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5174
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5175
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5176
vehicle_correlation_heatmap.png,Variables target and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5177
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5178
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5179
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5180
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5181
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5182
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5183
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5184
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5185
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5186
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5187
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5188
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5189
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5190
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5191
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5192
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5193
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5194
vehicle_correlation_heatmap.png,Variables target and MAX LENGTH ASPECT RATIO seem to be useful for classification tasks.,5195
vehicle_correlation_heatmap.png,Variables COMPACTNESS and SCATTER RATIO seem to be useful for classification tasks.,5196
vehicle_correlation_heatmap.png,Variables CIRCULARITY and SCATTER RATIO seem to be useful for classification tasks.,5197
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and SCATTER RATIO seem to be useful for classification tasks.,5198
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and SCATTER RATIO seem to be useful for classification tasks.,5199
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and SCATTER RATIO seem to be useful for classification tasks.,5200
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and SCATTER RATIO seem to be useful for classification tasks.,5201
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and SCATTER RATIO seem to be useful for classification tasks.,5202
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and SCATTER RATIO seem to be useful for classification tasks.,5203
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and SCATTER RATIO seem to be useful for classification tasks.,5204
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and SCATTER RATIO seem to be useful for classification tasks.,5205
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and SCATTER RATIO seem to be useful for classification tasks.,5206
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and SCATTER RATIO seem to be useful for classification tasks.,5207
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and SCATTER RATIO seem to be useful for classification tasks.,5208
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and SCATTER RATIO seem to be useful for classification tasks.,5209
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and SCATTER RATIO seem to be useful for classification tasks.,5210
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and SCATTER RATIO seem to be useful for classification tasks.,5211
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and SCATTER RATIO seem to be useful for classification tasks.,5212
vehicle_correlation_heatmap.png,Variables target and SCATTER RATIO seem to be useful for classification tasks.,5213
vehicle_correlation_heatmap.png,Variables COMPACTNESS and ELONGATEDNESS seem to be useful for classification tasks.,5214
vehicle_correlation_heatmap.png,Variables CIRCULARITY and ELONGATEDNESS seem to be useful for classification tasks.,5215
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and ELONGATEDNESS seem to be useful for classification tasks.,5216
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and ELONGATEDNESS seem to be useful for classification tasks.,5217
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and ELONGATEDNESS seem to be useful for classification tasks.,5218
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS seem to be useful for classification tasks.,5219
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and ELONGATEDNESS seem to be useful for classification tasks.,5220
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and ELONGATEDNESS seem to be useful for classification tasks.,5221
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and ELONGATEDNESS seem to be useful for classification tasks.,5222
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and ELONGATEDNESS seem to be useful for classification tasks.,5223
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and ELONGATEDNESS seem to be useful for classification tasks.,5224
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and ELONGATEDNESS seem to be useful for classification tasks.,5225
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and ELONGATEDNESS seem to be useful for classification tasks.,5226
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and ELONGATEDNESS seem to be useful for classification tasks.,5227
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and ELONGATEDNESS seem to be useful for classification tasks.,5228
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and ELONGATEDNESS seem to be useful for classification tasks.,5229
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and ELONGATEDNESS seem to be useful for classification tasks.,5230
vehicle_correlation_heatmap.png,Variables target and ELONGATEDNESS seem to be useful for classification tasks.,5231
vehicle_correlation_heatmap.png,Variables COMPACTNESS and PR AXISRECTANGULAR seem to be useful for classification tasks.,5232
vehicle_correlation_heatmap.png,Variables CIRCULARITY and PR AXISRECTANGULAR seem to be useful for classification tasks.,5233
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR seem to be useful for classification tasks.,5234
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and PR AXISRECTANGULAR seem to be useful for classification tasks.,5235
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and PR AXISRECTANGULAR seem to be useful for classification tasks.,5236
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR seem to be useful for classification tasks.,5237
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and PR AXISRECTANGULAR seem to be useful for classification tasks.,5238
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and PR AXISRECTANGULAR seem to be useful for classification tasks.,5239
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and PR AXISRECTANGULAR seem to be useful for classification tasks.,5240
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and PR AXISRECTANGULAR seem to be useful for classification tasks.,5241
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and PR AXISRECTANGULAR seem to be useful for classification tasks.,5242
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and PR AXISRECTANGULAR seem to be useful for classification tasks.,5243
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and PR AXISRECTANGULAR seem to be useful for classification tasks.,5244
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and PR AXISRECTANGULAR seem to be useful for classification tasks.,5245
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and PR AXISRECTANGULAR seem to be useful for classification tasks.,5246
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and PR AXISRECTANGULAR seem to be useful for classification tasks.,5247
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and PR AXISRECTANGULAR seem to be useful for classification tasks.,5248
vehicle_correlation_heatmap.png,Variables target and PR AXISRECTANGULAR seem to be useful for classification tasks.,5249
vehicle_correlation_heatmap.png,Variables COMPACTNESS and LENGTHRECTANGULAR seem to be useful for classification tasks.,5250
vehicle_correlation_heatmap.png,Variables CIRCULARITY and LENGTHRECTANGULAR seem to be useful for classification tasks.,5251
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and LENGTHRECTANGULAR seem to be useful for classification tasks.,5252
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and LENGTHRECTANGULAR seem to be useful for classification tasks.,5253
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR seem to be useful for classification tasks.,5254
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR seem to be useful for classification tasks.,5255
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and LENGTHRECTANGULAR seem to be useful for classification tasks.,5256
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and LENGTHRECTANGULAR seem to be useful for classification tasks.,5257
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and LENGTHRECTANGULAR seem to be useful for classification tasks.,5258
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and LENGTHRECTANGULAR seem to be useful for classification tasks.,5259
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and LENGTHRECTANGULAR seem to be useful for classification tasks.,5260
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and LENGTHRECTANGULAR seem to be useful for classification tasks.,5261
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and LENGTHRECTANGULAR seem to be useful for classification tasks.,5262
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and LENGTHRECTANGULAR seem to be useful for classification tasks.,5263
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and LENGTHRECTANGULAR seem to be useful for classification tasks.,5264
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and LENGTHRECTANGULAR seem to be useful for classification tasks.,5265
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and LENGTHRECTANGULAR seem to be useful for classification tasks.,5266
vehicle_correlation_heatmap.png,Variables target and LENGTHRECTANGULAR seem to be useful for classification tasks.,5267
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAJORVARIANCE seem to be useful for classification tasks.,5268
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAJORVARIANCE seem to be useful for classification tasks.,5269
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAJORVARIANCE seem to be useful for classification tasks.,5270
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAJORVARIANCE seem to be useful for classification tasks.,5271
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAJORVARIANCE seem to be useful for classification tasks.,5272
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MAJORVARIANCE seem to be useful for classification tasks.,5273
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAJORVARIANCE seem to be useful for classification tasks.,5274
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAJORVARIANCE seem to be useful for classification tasks.,5275
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORVARIANCE seem to be useful for classification tasks.,5276
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAJORVARIANCE seem to be useful for classification tasks.,5277
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORVARIANCE seem to be useful for classification tasks.,5278
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAJORVARIANCE seem to be useful for classification tasks.,5279
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MAJORVARIANCE seem to be useful for classification tasks.,5280
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAJORVARIANCE seem to be useful for classification tasks.,5281
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAJORVARIANCE seem to be useful for classification tasks.,5282
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MAJORVARIANCE seem to be useful for classification tasks.,5283
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAJORVARIANCE seem to be useful for classification tasks.,5284
vehicle_correlation_heatmap.png,Variables target and MAJORVARIANCE seem to be useful for classification tasks.,5285
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MINORVARIANCE seem to be useful for classification tasks.,5286
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MINORVARIANCE seem to be useful for classification tasks.,5287
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MINORVARIANCE seem to be useful for classification tasks.,5288
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MINORVARIANCE seem to be useful for classification tasks.,5289
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MINORVARIANCE seem to be useful for classification tasks.,5290
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE seem to be useful for classification tasks.,5291
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MINORVARIANCE seem to be useful for classification tasks.,5292
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORVARIANCE seem to be useful for classification tasks.,5293
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MINORVARIANCE seem to be useful for classification tasks.,5294
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MINORVARIANCE seem to be useful for classification tasks.,5295
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MINORVARIANCE seem to be useful for classification tasks.,5296
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MINORVARIANCE seem to be useful for classification tasks.,5297
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MINORVARIANCE seem to be useful for classification tasks.,5298
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MINORVARIANCE seem to be useful for classification tasks.,5299
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MINORVARIANCE seem to be useful for classification tasks.,5300
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MINORVARIANCE seem to be useful for classification tasks.,5301
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MINORVARIANCE seem to be useful for classification tasks.,5302
vehicle_correlation_heatmap.png,Variables target and MINORVARIANCE seem to be useful for classification tasks.,5303
vehicle_correlation_heatmap.png,Variables COMPACTNESS and GYRATIONRADIUS seem to be useful for classification tasks.,5304
vehicle_correlation_heatmap.png,Variables CIRCULARITY and GYRATIONRADIUS seem to be useful for classification tasks.,5305
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and GYRATIONRADIUS seem to be useful for classification tasks.,5306
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and GYRATIONRADIUS seem to be useful for classification tasks.,5307
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS seem to be useful for classification tasks.,5308
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS seem to be useful for classification tasks.,5309
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and GYRATIONRADIUS seem to be useful for classification tasks.,5310
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and GYRATIONRADIUS seem to be useful for classification tasks.,5311
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and GYRATIONRADIUS seem to be useful for classification tasks.,5312
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and GYRATIONRADIUS seem to be useful for classification tasks.,5313
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and GYRATIONRADIUS seem to be useful for classification tasks.,5314
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and GYRATIONRADIUS seem to be useful for classification tasks.,5315
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and GYRATIONRADIUS seem to be useful for classification tasks.,5316
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and GYRATIONRADIUS seem to be useful for classification tasks.,5317
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and GYRATIONRADIUS seem to be useful for classification tasks.,5318
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and GYRATIONRADIUS seem to be useful for classification tasks.,5319
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and GYRATIONRADIUS seem to be useful for classification tasks.,5320
vehicle_correlation_heatmap.png,Variables target and GYRATIONRADIUS seem to be useful for classification tasks.,5321
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAJORSKEWNESS seem to be useful for classification tasks.,5322
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAJORSKEWNESS seem to be useful for classification tasks.,5323
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAJORSKEWNESS seem to be useful for classification tasks.,5324
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAJORSKEWNESS seem to be useful for classification tasks.,5325
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS seem to be useful for classification tasks.,5326
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MAJORSKEWNESS seem to be useful for classification tasks.,5327
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAJORSKEWNESS seem to be useful for classification tasks.,5328
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAJORSKEWNESS seem to be useful for classification tasks.,5329
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORSKEWNESS seem to be useful for classification tasks.,5330
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAJORSKEWNESS seem to be useful for classification tasks.,5331
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MAJORSKEWNESS seem to be useful for classification tasks.,5332
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORSKEWNESS seem to be useful for classification tasks.,5333
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAJORSKEWNESS seem to be useful for classification tasks.,5334
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAJORSKEWNESS seem to be useful for classification tasks.,5335
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAJORSKEWNESS seem to be useful for classification tasks.,5336
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MAJORSKEWNESS seem to be useful for classification tasks.,5337
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAJORSKEWNESS seem to be useful for classification tasks.,5338
vehicle_correlation_heatmap.png,Variables target and MAJORSKEWNESS seem to be useful for classification tasks.,5339
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MINORSKEWNESS seem to be useful for classification tasks.,5340
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MINORSKEWNESS seem to be useful for classification tasks.,5341
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MINORSKEWNESS seem to be useful for classification tasks.,5342
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MINORSKEWNESS seem to be useful for classification tasks.,5343
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MINORSKEWNESS seem to be useful for classification tasks.,5344
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS seem to be useful for classification tasks.,5345
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MINORSKEWNESS seem to be useful for classification tasks.,5346
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORSKEWNESS seem to be useful for classification tasks.,5347
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MINORSKEWNESS seem to be useful for classification tasks.,5348
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MINORSKEWNESS seem to be useful for classification tasks.,5349
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MINORSKEWNESS seem to be useful for classification tasks.,5350
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MINORSKEWNESS seem to be useful for classification tasks.,5351
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MINORSKEWNESS seem to be useful for classification tasks.,5352
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MINORSKEWNESS seem to be useful for classification tasks.,5353
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MINORSKEWNESS seem to be useful for classification tasks.,5354
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MINORSKEWNESS seem to be useful for classification tasks.,5355
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MINORSKEWNESS seem to be useful for classification tasks.,5356
vehicle_correlation_heatmap.png,Variables target and MINORSKEWNESS seem to be useful for classification tasks.,5357
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MINORKURTOSIS seem to be useful for classification tasks.,5358
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MINORKURTOSIS seem to be useful for classification tasks.,5359
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MINORKURTOSIS seem to be useful for classification tasks.,5360
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MINORKURTOSIS seem to be useful for classification tasks.,5361
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MINORKURTOSIS seem to be useful for classification tasks.,5362
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MINORKURTOSIS seem to be useful for classification tasks.,5363
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MINORKURTOSIS seem to be useful for classification tasks.,5364
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORKURTOSIS seem to be useful for classification tasks.,5365
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MINORKURTOSIS seem to be useful for classification tasks.,5366
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MINORKURTOSIS seem to be useful for classification tasks.,5367
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MINORKURTOSIS seem to be useful for classification tasks.,5368
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MINORKURTOSIS seem to be useful for classification tasks.,5369
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MINORKURTOSIS seem to be useful for classification tasks.,5370
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MINORKURTOSIS seem to be useful for classification tasks.,5371
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MINORKURTOSIS seem to be useful for classification tasks.,5372
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MINORKURTOSIS seem to be useful for classification tasks.,5373
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MINORKURTOSIS seem to be useful for classification tasks.,5374
vehicle_correlation_heatmap.png,Variables target and MINORKURTOSIS seem to be useful for classification tasks.,5375
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAJORKURTOSIS seem to be useful for classification tasks.,5376
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAJORKURTOSIS seem to be useful for classification tasks.,5377
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAJORKURTOSIS seem to be useful for classification tasks.,5378
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAJORKURTOSIS seem to be useful for classification tasks.,5379
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS seem to be useful for classification tasks.,5380
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MAJORKURTOSIS seem to be useful for classification tasks.,5381
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAJORKURTOSIS seem to be useful for classification tasks.,5382
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAJORKURTOSIS seem to be useful for classification tasks.,5383
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORKURTOSIS seem to be useful for classification tasks.,5384
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAJORKURTOSIS seem to be useful for classification tasks.,5385
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MAJORKURTOSIS seem to be useful for classification tasks.,5386
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORKURTOSIS seem to be useful for classification tasks.,5387
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAJORKURTOSIS seem to be useful for classification tasks.,5388
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MAJORKURTOSIS seem to be useful for classification tasks.,5389
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAJORKURTOSIS seem to be useful for classification tasks.,5390
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAJORKURTOSIS seem to be useful for classification tasks.,5391
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAJORKURTOSIS seem to be useful for classification tasks.,5392
vehicle_correlation_heatmap.png,Variables target and MAJORKURTOSIS seem to be useful for classification tasks.,5393
vehicle_correlation_heatmap.png,Variables COMPACTNESS and HOLLOWS RATIO seem to be useful for classification tasks.,5394
vehicle_correlation_heatmap.png,Variables CIRCULARITY and HOLLOWS RATIO seem to be useful for classification tasks.,5395
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and HOLLOWS RATIO seem to be useful for classification tasks.,5396
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and HOLLOWS RATIO seem to be useful for classification tasks.,5397
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO seem to be useful for classification tasks.,5398
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO seem to be useful for classification tasks.,5399
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and HOLLOWS RATIO seem to be useful for classification tasks.,5400
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and HOLLOWS RATIO seem to be useful for classification tasks.,5401
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and HOLLOWS RATIO seem to be useful for classification tasks.,5402
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and HOLLOWS RATIO seem to be useful for classification tasks.,5403
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and HOLLOWS RATIO seem to be useful for classification tasks.,5404
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and HOLLOWS RATIO seem to be useful for classification tasks.,5405
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and HOLLOWS RATIO seem to be useful for classification tasks.,5406
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and HOLLOWS RATIO seem to be useful for classification tasks.,5407
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and HOLLOWS RATIO seem to be useful for classification tasks.,5408
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and HOLLOWS RATIO seem to be useful for classification tasks.,5409
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and HOLLOWS RATIO seem to be useful for classification tasks.,5410
vehicle_correlation_heatmap.png,Variables target and HOLLOWS RATIO seem to be useful for classification tasks.,5411
vehicle_correlation_heatmap.png,Variables COMPACTNESS and target seem to be useful for classification tasks.,5412
vehicle_correlation_heatmap.png,Variables CIRCULARITY and target seem to be useful for classification tasks.,5413
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and target seem to be useful for classification tasks.,5414
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and target seem to be useful for classification tasks.,5415
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and target seem to be useful for classification tasks.,5416
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and target seem to be useful for classification tasks.,5417
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and target seem to be useful for classification tasks.,5418
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and target seem to be useful for classification tasks.,5419
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and target seem to be useful for classification tasks.,5420
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and target seem to be useful for classification tasks.,5421
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and target seem to be useful for classification tasks.,5422
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and target seem to be useful for classification tasks.,5423
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and target seem to be useful for classification tasks.,5424
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and target seem to be useful for classification tasks.,5425
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and target seem to be useful for classification tasks.,5426
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and target seem to be useful for classification tasks.,5427
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and target seem to be useful for classification tasks.,5428
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and target seem to be useful for classification tasks.,5429
vehicle_correlation_heatmap.png,Variable COMPACTNESS seems to be relevant for the majority of mining tasks.,5430
vehicle_correlation_heatmap.png,Variable CIRCULARITY seems to be relevant for the majority of mining tasks.,5431
vehicle_correlation_heatmap.png,Variable DISTANCE CIRCULARITY seems to be relevant for the majority of mining tasks.,5432
vehicle_correlation_heatmap.png,Variable RADIUS RATIO seems to be relevant for the majority of mining tasks.,5433
vehicle_correlation_heatmap.png,Variable PR AXIS ASPECT RATIO seems to be relevant for the majority of mining tasks.,5434
vehicle_correlation_heatmap.png,Variable MAX LENGTH ASPECT RATIO seems to be relevant for the majority of mining tasks.,5435
vehicle_correlation_heatmap.png,Variable SCATTER RATIO seems to be relevant for the majority of mining tasks.,5436
vehicle_correlation_heatmap.png,Variable ELONGATEDNESS seems to be relevant for the majority of mining tasks.,5437
vehicle_correlation_heatmap.png,Variable PR AXISRECTANGULAR seems to be relevant for the majority of mining tasks.,5438
vehicle_correlation_heatmap.png,Variable LENGTHRECTANGULAR seems to be relevant for the majority of mining tasks.,5439
vehicle_correlation_heatmap.png,Variable MAJORVARIANCE seems to be relevant for the majority of mining tasks.,5440
vehicle_correlation_heatmap.png,Variable MINORVARIANCE seems to be relevant for the majority of mining tasks.,5441
vehicle_correlation_heatmap.png,Variable GYRATIONRADIUS seems to be relevant for the majority of mining tasks.,5442
vehicle_correlation_heatmap.png,Variable MAJORSKEWNESS seems to be relevant for the majority of mining tasks.,5443
vehicle_correlation_heatmap.png,Variable MINORSKEWNESS seems to be relevant for the majority of mining tasks.,5444
vehicle_correlation_heatmap.png,Variable MINORKURTOSIS seems to be relevant for the majority of mining tasks.,5445
vehicle_correlation_heatmap.png,Variable MAJORKURTOSIS seems to be relevant for the majority of mining tasks.,5446
vehicle_correlation_heatmap.png,Variable HOLLOWS RATIO seems to be relevant for the majority of mining tasks.,5447
vehicle_correlation_heatmap.png,Variable target seems to be relevant for the majority of mining tasks.,5448
vehicle_decision_tree.png,Variable COMPACTNESS is one of the most relevant variables.,5449
vehicle_decision_tree.png,Variable CIRCULARITY is one of the most relevant variables.,5450
vehicle_decision_tree.png,Variable DISTANCE CIRCULARITY is one of the most relevant variables.,5451
vehicle_decision_tree.png,Variable RADIUS RATIO is one of the most relevant variables.,5452
vehicle_decision_tree.png,Variable PR AXIS ASPECT RATIO is one of the most relevant variables.,5453
vehicle_decision_tree.png,Variable MAX LENGTH ASPECT RATIO is one of the most relevant variables.,5454
vehicle_decision_tree.png,Variable SCATTER RATIO is one of the most relevant variables.,5455
vehicle_decision_tree.png,Variable ELONGATEDNESS is one of the most relevant variables.,5456
vehicle_decision_tree.png,Variable PR AXISRECTANGULAR is one of the most relevant variables.,5457
vehicle_decision_tree.png,Variable LENGTHRECTANGULAR is one of the most relevant variables.,5458
vehicle_decision_tree.png,Variable MAJORVARIANCE is one of the most relevant variables.,5459
vehicle_decision_tree.png,Variable MINORVARIANCE is one of the most relevant variables.,5460
vehicle_decision_tree.png,Variable GYRATIONRADIUS is one of the most relevant variables.,5461
vehicle_decision_tree.png,Variable MAJORSKEWNESS is one of the most relevant variables.,5462
vehicle_decision_tree.png,Variable MINORSKEWNESS is one of the most relevant variables.,5463
vehicle_decision_tree.png,Variable MINORKURTOSIS is one of the most relevant variables.,5464
vehicle_decision_tree.png,Variable MAJORKURTOSIS is one of the most relevant variables.,5465
vehicle_decision_tree.png,Variable HOLLOWS RATIO is one of the most relevant variables.,5466
vehicle_decision_tree.png,Variable target is one of the most relevant variables.,5467
vehicle_decision_tree.png,It is possible to state that COMPACTNESS is the first most discriminative variable regarding the class.,5468
vehicle_decision_tree.png,It is possible to state that CIRCULARITY is the first most discriminative variable regarding the class.,5469
vehicle_decision_tree.png,It is possible to state that DISTANCE CIRCULARITY is the first most discriminative variable regarding the class.,5470
vehicle_decision_tree.png,It is possible to state that RADIUS RATIO is the first most discriminative variable regarding the class.,5471
vehicle_decision_tree.png,It is possible to state that PR AXIS ASPECT RATIO is the first most discriminative variable regarding the class.,5472
vehicle_decision_tree.png,It is possible to state that MAX LENGTH ASPECT RATIO is the first most discriminative variable regarding the class.,5473
vehicle_decision_tree.png,It is possible to state that SCATTER RATIO is the first most discriminative variable regarding the class.,5474
vehicle_decision_tree.png,It is possible to state that ELONGATEDNESS is the first most discriminative variable regarding the class.,5475
vehicle_decision_tree.png,It is possible to state that PR AXISRECTANGULAR is the first most discriminative variable regarding the class.,5476
vehicle_decision_tree.png,It is possible to state that LENGTHRECTANGULAR is the first most discriminative variable regarding the class.,5477
vehicle_decision_tree.png,It is possible to state that MAJORVARIANCE is the first most discriminative variable regarding the class.,5478
vehicle_decision_tree.png,It is possible to state that MINORVARIANCE is the first most discriminative variable regarding the class.,5479
vehicle_decision_tree.png,It is possible to state that GYRATIONRADIUS is the first most discriminative variable regarding the class.,5480
vehicle_decision_tree.png,It is possible to state that MAJORSKEWNESS is the first most discriminative variable regarding the class.,5481
vehicle_decision_tree.png,It is possible to state that MINORSKEWNESS is the first most discriminative variable regarding the class.,5482
vehicle_decision_tree.png,It is possible to state that MINORKURTOSIS is the first most discriminative variable regarding the class.,5483
vehicle_decision_tree.png,It is possible to state that MAJORKURTOSIS is the first most discriminative variable regarding the class.,5484
vehicle_decision_tree.png,It is possible to state that HOLLOWS RATIO is the first most discriminative variable regarding the class.,5485
vehicle_decision_tree.png,It is possible to state that target is the first most discriminative variable regarding the class.,5486
vehicle_decision_tree.png,It is possible to state that COMPACTNESS is the second most discriminative variable regarding the class.,5487
vehicle_decision_tree.png,It is possible to state that CIRCULARITY is the second most discriminative variable regarding the class.,5488
vehicle_decision_tree.png,It is possible to state that DISTANCE CIRCULARITY is the second most discriminative variable regarding the class.,5489
vehicle_decision_tree.png,It is possible to state that RADIUS RATIO is the second most discriminative variable regarding the class.,5490
vehicle_decision_tree.png,It is possible to state that PR AXIS ASPECT RATIO is the second most discriminative variable regarding the class.,5491
vehicle_decision_tree.png,It is possible to state that MAX LENGTH ASPECT RATIO is the second most discriminative variable regarding the class.,5492
vehicle_decision_tree.png,It is possible to state that SCATTER RATIO is the second most discriminative variable regarding the class.,5493
vehicle_decision_tree.png,It is possible to state that ELONGATEDNESS is the second most discriminative variable regarding the class.,5494
vehicle_decision_tree.png,It is possible to state that PR AXISRECTANGULAR is the second most discriminative variable regarding the class.,5495
vehicle_decision_tree.png,It is possible to state that LENGTHRECTANGULAR is the second most discriminative variable regarding the class.,5496
vehicle_decision_tree.png,It is possible to state that MAJORVARIANCE is the second most discriminative variable regarding the class.,5497
vehicle_decision_tree.png,It is possible to state that MINORVARIANCE is the second most discriminative variable regarding the class.,5498
vehicle_decision_tree.png,It is possible to state that GYRATIONRADIUS is the second most discriminative variable regarding the class.,5499
vehicle_decision_tree.png,It is possible to state that MAJORSKEWNESS is the second most discriminative variable regarding the class.,5500
vehicle_decision_tree.png,It is possible to state that MINORSKEWNESS is the second most discriminative variable regarding the class.,5501
vehicle_decision_tree.png,It is possible to state that MINORKURTOSIS is the second most discriminative variable regarding the class.,5502
vehicle_decision_tree.png,It is possible to state that MAJORKURTOSIS is the second most discriminative variable regarding the class.,5503
vehicle_decision_tree.png,It is possible to state that HOLLOWS RATIO is the second most discriminative variable regarding the class.,5504
vehicle_decision_tree.png,It is possible to state that target is the second most discriminative variable regarding the class.,5505
vehicle_decision_tree.png,"The variable COMPACTNESS discriminates between the target values, as shown in the decision tree.",5506
vehicle_decision_tree.png,"The variable CIRCULARITY discriminates between the target values, as shown in the decision tree.",5507
vehicle_decision_tree.png,"The variable DISTANCE CIRCULARITY discriminates between the target values, as shown in the decision tree.",5508
vehicle_decision_tree.png,"The variable RADIUS RATIO discriminates between the target values, as shown in the decision tree.",5509
vehicle_decision_tree.png,"The variable PR AXIS ASPECT RATIO discriminates between the target values, as shown in the decision tree.",5510
vehicle_decision_tree.png,"The variable MAX LENGTH ASPECT RATIO discriminates between the target values, as shown in the decision tree.",5511
vehicle_decision_tree.png,"The variable SCATTER RATIO discriminates between the target values, as shown in the decision tree.",5512
vehicle_decision_tree.png,"The variable ELONGATEDNESS discriminates between the target values, as shown in the decision tree.",5513
vehicle_decision_tree.png,"The variable PR AXISRECTANGULAR discriminates between the target values, as shown in the decision tree.",5514
vehicle_decision_tree.png,"The variable LENGTHRECTANGULAR discriminates between the target values, as shown in the decision tree.",5515
vehicle_decision_tree.png,"The variable MAJORVARIANCE discriminates between the target values, as shown in the decision tree.",5516
vehicle_decision_tree.png,"The variable MINORVARIANCE discriminates between the target values, as shown in the decision tree.",5517
vehicle_decision_tree.png,"The variable GYRATIONRADIUS discriminates between the target values, as shown in the decision tree.",5518
vehicle_decision_tree.png,"The variable MAJORSKEWNESS discriminates between the target values, as shown in the decision tree.",5519
vehicle_decision_tree.png,"The variable MINORSKEWNESS discriminates between the target values, as shown in the decision tree.",5520
vehicle_decision_tree.png,"The variable MINORKURTOSIS discriminates between the target values, as shown in the decision tree.",5521
vehicle_decision_tree.png,"The variable MAJORKURTOSIS discriminates between the target values, as shown in the decision tree.",5522
vehicle_decision_tree.png,"The variable HOLLOWS RATIO discriminates between the target values, as shown in the decision tree.",5523
vehicle_decision_tree.png,"The variable target discriminates between the target values, as shown in the decision tree.",5524
vehicle_decision_tree.png,The variable COMPACTNESS seems to be one of the two most relevant features.,5525
vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the two most relevant features.,5526
vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the two most relevant features.,5527
vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the two most relevant features.,5528
vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the two most relevant features.,5529
vehicle_decision_tree.png,The variable MAX LENGTH ASPECT RATIO seems to be one of the two most relevant features.,5530
vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the two most relevant features.,5531
vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the two most relevant features.,5532
vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the two most relevant features.,5533
vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the two most relevant features.,5534
vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the two most relevant features.,5535
vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the two most relevant features.,5536
vehicle_decision_tree.png,The variable GYRATIONRADIUS seems to be one of the two most relevant features.,5537
vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the two most relevant features.,5538
vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the two most relevant features.,5539
vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the two most relevant features.,5540
vehicle_decision_tree.png,The variable MAJORKURTOSIS seems to be one of the two most relevant features.,5541
vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the two most relevant features.,5542
vehicle_decision_tree.png,The variable target seems to be one of the two most relevant features.,5543
vehicle_decision_tree.png,The variable COMPACTNESS seems to be one of the three most relevant features.,5544
vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the three most relevant features.,5545
vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the three most relevant features.,5546
vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the three most relevant features.,5547
vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the three most relevant features.,5548
vehicle_decision_tree.png,The variable MAX LENGTH ASPECT RATIO seems to be one of the three most relevant features.,5549
vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the three most relevant features.,5550
vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the three most relevant features.,5551
vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the three most relevant features.,5552
vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the three most relevant features.,5553
vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the three most relevant features.,5554
vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the three most relevant features.,5555
vehicle_decision_tree.png,The variable GYRATIONRADIUS seems to be one of the three most relevant features.,5556
vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the three most relevant features.,5557
vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the three most relevant features.,5558
vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the three most relevant features.,5559
vehicle_decision_tree.png,The variable MAJORKURTOSIS seems to be one of the three most relevant features.,5560
vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the three most relevant features.,5561
vehicle_decision_tree.png,The variable target seems to be one of the three most relevant features.,5562
vehicle_decision_tree.png,The variable COMPACTNESS seems to be one of the four most relevant features.,5563
vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the four most relevant features.,5564
vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the four most relevant features.,5565
vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the four most relevant features.,5566
vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the four most relevant features.,5567
vehicle_decision_tree.png,The variable MAX LENGTH ASPECT RATIO seems to be one of the four most relevant features.,5568
vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the four most relevant features.,5569
vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the four most relevant features.,5570
vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the four most relevant features.,5571
vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the four most relevant features.,5572
vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the four most relevant features.,5573
vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the four most relevant features.,5574
vehicle_decision_tree.png,The variable GYRATIONRADIUS seems to be one of the four most relevant features.,5575
vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the four most relevant features.,5576
vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the four most relevant features.,5577
vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the four most relevant features.,5578
vehicle_decision_tree.png,The variable MAJORKURTOSIS seems to be one of the four most relevant features.,5579
vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the four most relevant features.,5580
vehicle_decision_tree.png,The variable target seems to be one of the four most relevant features.,5581
vehicle_decision_tree.png,The variable COMPACTNESS seems to be one of the five most relevant features.,5582
vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the five most relevant features.,5583
vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the five most relevant features.,5584
vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the five most relevant features.,5585
vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the five most relevant features.,5586
vehicle_decision_tree.png,The variable MAX LENGTH ASPECT RATIO seems to be one of the five most relevant features.,5587
vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the five most relevant features.,5588
vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the five most relevant features.,5589
vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the five most relevant features.,5590
vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the five most relevant features.,5591
vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the five most relevant features.,5592
vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the five most relevant features.,5593
vehicle_decision_tree.png,The variable GYRATIONRADIUS seems to be one of the five most relevant features.,5594
vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the five most relevant features.,5595
vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the five most relevant features.,5596
vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the five most relevant features.,5597
vehicle_decision_tree.png,The variable MAJORKURTOSIS seems to be one of the five most relevant features.,5598
vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the five most relevant features.,5599
vehicle_decision_tree.png,The variable target seems to be one of the five most relevant features.,5600
vehicle_decision_tree.png,It is clear that variable COMPACTNESS is one of the two most relevant features.,5601
vehicle_decision_tree.png,It is clear that variable CIRCULARITY is one of the two most relevant features.,5602
vehicle_decision_tree.png,It is clear that variable DISTANCE CIRCULARITY is one of the two most relevant features.,5603
vehicle_decision_tree.png,It is clear that variable RADIUS RATIO is one of the two most relevant features.,5604
vehicle_decision_tree.png,It is clear that variable PR AXIS ASPECT RATIO is one of the two most relevant features.,5605
vehicle_decision_tree.png,It is clear that variable MAX LENGTH ASPECT RATIO is one of the two most relevant features.,5606
vehicle_decision_tree.png,It is clear that variable SCATTER RATIO is one of the two most relevant features.,5607
vehicle_decision_tree.png,It is clear that variable ELONGATEDNESS is one of the two most relevant features.,5608
vehicle_decision_tree.png,It is clear that variable PR AXISRECTANGULAR is one of the two most relevant features.,5609
vehicle_decision_tree.png,It is clear that variable LENGTHRECTANGULAR is one of the two most relevant features.,5610
vehicle_decision_tree.png,It is clear that variable MAJORVARIANCE is one of the two most relevant features.,5611
vehicle_decision_tree.png,It is clear that variable MINORVARIANCE is one of the two most relevant features.,5612
vehicle_decision_tree.png,It is clear that variable GYRATIONRADIUS is one of the two most relevant features.,5613
vehicle_decision_tree.png,It is clear that variable MAJORSKEWNESS is one of the two most relevant features.,5614
vehicle_decision_tree.png,It is clear that variable MINORSKEWNESS is one of the two most relevant features.,5615
vehicle_decision_tree.png,It is clear that variable MINORKURTOSIS is one of the two most relevant features.,5616
vehicle_decision_tree.png,It is clear that variable MAJORKURTOSIS is one of the two most relevant features.,5617
vehicle_decision_tree.png,It is clear that variable HOLLOWS RATIO is one of the two most relevant features.,5618
vehicle_decision_tree.png,It is clear that variable target is one of the two most relevant features.,5619
vehicle_decision_tree.png,It is clear that variable COMPACTNESS is one of the three most relevant features.,5620
vehicle_decision_tree.png,It is clear that variable CIRCULARITY is one of the three most relevant features.,5621
vehicle_decision_tree.png,It is clear that variable DISTANCE CIRCULARITY is one of the three most relevant features.,5622
vehicle_decision_tree.png,It is clear that variable RADIUS RATIO is one of the three most relevant features.,5623
vehicle_decision_tree.png,It is clear that variable PR AXIS ASPECT RATIO is one of the three most relevant features.,5624
vehicle_decision_tree.png,It is clear that variable MAX LENGTH ASPECT RATIO is one of the three most relevant features.,5625
vehicle_decision_tree.png,It is clear that variable SCATTER RATIO is one of the three most relevant features.,5626
vehicle_decision_tree.png,It is clear that variable ELONGATEDNESS is one of the three most relevant features.,5627
vehicle_decision_tree.png,It is clear that variable PR AXISRECTANGULAR is one of the three most relevant features.,5628
vehicle_decision_tree.png,It is clear that variable LENGTHRECTANGULAR is one of the three most relevant features.,5629
vehicle_decision_tree.png,It is clear that variable MAJORVARIANCE is one of the three most relevant features.,5630
vehicle_decision_tree.png,It is clear that variable MINORVARIANCE is one of the three most relevant features.,5631
vehicle_decision_tree.png,It is clear that variable GYRATIONRADIUS is one of the three most relevant features.,5632
vehicle_decision_tree.png,It is clear that variable MAJORSKEWNESS is one of the three most relevant features.,5633
vehicle_decision_tree.png,It is clear that variable MINORSKEWNESS is one of the three most relevant features.,5634
vehicle_decision_tree.png,It is clear that variable MINORKURTOSIS is one of the three most relevant features.,5635
vehicle_decision_tree.png,It is clear that variable MAJORKURTOSIS is one of the three most relevant features.,5636
vehicle_decision_tree.png,It is clear that variable HOLLOWS RATIO is one of the three most relevant features.,5637
vehicle_decision_tree.png,It is clear that variable target is one of the three most relevant features.,5638
vehicle_decision_tree.png,It is clear that variable COMPACTNESS is one of the four most relevant features.,5639
vehicle_decision_tree.png,It is clear that variable CIRCULARITY is one of the four most relevant features.,5640
vehicle_decision_tree.png,It is clear that variable DISTANCE CIRCULARITY is one of the four most relevant features.,5641
vehicle_decision_tree.png,It is clear that variable RADIUS RATIO is one of the four most relevant features.,5642
vehicle_decision_tree.png,It is clear that variable PR AXIS ASPECT RATIO is one of the four most relevant features.,5643
vehicle_decision_tree.png,It is clear that variable MAX LENGTH ASPECT RATIO is one of the four most relevant features.,5644
vehicle_decision_tree.png,It is clear that variable SCATTER RATIO is one of the four most relevant features.,5645
vehicle_decision_tree.png,It is clear that variable ELONGATEDNESS is one of the four most relevant features.,5646
vehicle_decision_tree.png,It is clear that variable PR AXISRECTANGULAR is one of the four most relevant features.,5647
vehicle_decision_tree.png,It is clear that variable LENGTHRECTANGULAR is one of the four most relevant features.,5648
vehicle_decision_tree.png,It is clear that variable MAJORVARIANCE is one of the four most relevant features.,5649
vehicle_decision_tree.png,It is clear that variable MINORVARIANCE is one of the four most relevant features.,5650
vehicle_decision_tree.png,It is clear that variable GYRATIONRADIUS is one of the four most relevant features.,5651
vehicle_decision_tree.png,It is clear that variable MAJORSKEWNESS is one of the four most relevant features.,5652
vehicle_decision_tree.png,It is clear that variable MINORSKEWNESS is one of the four most relevant features.,5653
vehicle_decision_tree.png,It is clear that variable MINORKURTOSIS is one of the four most relevant features.,5654
vehicle_decision_tree.png,It is clear that variable MAJORKURTOSIS is one of the four most relevant features.,5655
vehicle_decision_tree.png,It is clear that variable HOLLOWS RATIO is one of the four most relevant features.,5656
vehicle_decision_tree.png,It is clear that variable target is one of the four most relevant features.,5657
vehicle_decision_tree.png,It is clear that variable COMPACTNESS is one of the five most relevant features.,5658
vehicle_decision_tree.png,It is clear that variable CIRCULARITY is one of the five most relevant features.,5659
vehicle_decision_tree.png,It is clear that variable DISTANCE CIRCULARITY is one of the five most relevant features.,5660
vehicle_decision_tree.png,It is clear that variable RADIUS RATIO is one of the five most relevant features.,5661
vehicle_decision_tree.png,It is clear that variable PR AXIS ASPECT RATIO is one of the five most relevant features.,5662
vehicle_decision_tree.png,It is clear that variable MAX LENGTH ASPECT RATIO is one of the five most relevant features.,5663
vehicle_decision_tree.png,It is clear that variable SCATTER RATIO is one of the five most relevant features.,5664
vehicle_decision_tree.png,It is clear that variable ELONGATEDNESS is one of the five most relevant features.,5665
vehicle_decision_tree.png,It is clear that variable PR AXISRECTANGULAR is one of the five most relevant features.,5666
vehicle_decision_tree.png,It is clear that variable LENGTHRECTANGULAR is one of the five most relevant features.,5667
vehicle_decision_tree.png,It is clear that variable MAJORVARIANCE is one of the five most relevant features.,5668
vehicle_decision_tree.png,It is clear that variable MINORVARIANCE is one of the five most relevant features.,5669
vehicle_decision_tree.png,It is clear that variable GYRATIONRADIUS is one of the five most relevant features.,5670
vehicle_decision_tree.png,It is clear that variable MAJORSKEWNESS is one of the five most relevant features.,5671
vehicle_decision_tree.png,It is clear that variable MINORSKEWNESS is one of the five most relevant features.,5672
vehicle_decision_tree.png,It is clear that variable MINORKURTOSIS is one of the five most relevant features.,5673
vehicle_decision_tree.png,It is clear that variable MAJORKURTOSIS is one of the five most relevant features.,5674
vehicle_decision_tree.png,It is clear that variable HOLLOWS RATIO is one of the five most relevant features.,5675
vehicle_decision_tree.png,It is clear that variable target is one of the five most relevant features.,5676
vehicle_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",5677
vehicle_correlation_heatmap.png,Variables CIRCULARITY and COMPACTNESS are redundant.,5678
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and COMPACTNESS are redundant.,5679
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and COMPACTNESS are redundant.,5680
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and COMPACTNESS are redundant.,5681
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and COMPACTNESS are redundant.,5682
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and COMPACTNESS are redundant.,5683
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and COMPACTNESS are redundant.,5684
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and COMPACTNESS are redundant.,5685
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and COMPACTNESS are redundant.,5686
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and COMPACTNESS are redundant.,5687
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and COMPACTNESS are redundant.,5688
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and COMPACTNESS are redundant.,5689
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and COMPACTNESS are redundant.,5690
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and COMPACTNESS are redundant.,5691
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and COMPACTNESS are redundant.,5692
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and COMPACTNESS are redundant.,5693
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and COMPACTNESS are redundant.,5694
vehicle_correlation_heatmap.png,Variables target and COMPACTNESS are redundant.,5695
vehicle_correlation_heatmap.png,Variables COMPACTNESS and CIRCULARITY are redundant.,5696
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and CIRCULARITY are redundant.,5697
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and CIRCULARITY are redundant.,5698
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and CIRCULARITY are redundant.,5699
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and CIRCULARITY are redundant.,5700
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and CIRCULARITY are redundant.,5701
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and CIRCULARITY are redundant.,5702
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and CIRCULARITY are redundant.,5703
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and CIRCULARITY are redundant.,5704
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and CIRCULARITY are redundant.,5705
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and CIRCULARITY are redundant.,5706
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and CIRCULARITY are redundant.,5707
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and CIRCULARITY are redundant.,5708
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and CIRCULARITY are redundant.,5709
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and CIRCULARITY are redundant.,5710
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and CIRCULARITY are redundant.,5711
vehicle_correlation_heatmap.png,Variables target and CIRCULARITY are redundant.,5712
vehicle_correlation_heatmap.png,Variables COMPACTNESS and DISTANCE CIRCULARITY are redundant.,5713
vehicle_correlation_heatmap.png,Variables CIRCULARITY and DISTANCE CIRCULARITY are redundant.,5714
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and DISTANCE CIRCULARITY are redundant.,5715
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and DISTANCE CIRCULARITY are redundant.,5716
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and DISTANCE CIRCULARITY are redundant.,5717
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and DISTANCE CIRCULARITY are redundant.,5718
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and DISTANCE CIRCULARITY are redundant.,5719
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and DISTANCE CIRCULARITY are redundant.,5720
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and DISTANCE CIRCULARITY are redundant.,5721
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and DISTANCE CIRCULARITY are redundant.,5722
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and DISTANCE CIRCULARITY are redundant.,5723
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and DISTANCE CIRCULARITY are redundant.,5724
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and DISTANCE CIRCULARITY are redundant.,5725
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and DISTANCE CIRCULARITY are redundant.,5726
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and DISTANCE CIRCULARITY are redundant.,5727
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and DISTANCE CIRCULARITY are redundant.,5728
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and DISTANCE CIRCULARITY are redundant.,5729
vehicle_correlation_heatmap.png,Variables target and DISTANCE CIRCULARITY are redundant.,5730
vehicle_correlation_heatmap.png,Variables COMPACTNESS and RADIUS RATIO are redundant.,5731
vehicle_correlation_heatmap.png,Variables CIRCULARITY and RADIUS RATIO are redundant.,5732
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant.,5733
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and RADIUS RATIO are redundant.,5734
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and RADIUS RATIO are redundant.,5735
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and RADIUS RATIO are redundant.,5736
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and RADIUS RATIO are redundant.,5737
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and RADIUS RATIO are redundant.,5738
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and RADIUS RATIO are redundant.,5739
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and RADIUS RATIO are redundant.,5740
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and RADIUS RATIO are redundant.,5741
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and RADIUS RATIO are redundant.,5742
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and RADIUS RATIO are redundant.,5743
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and RADIUS RATIO are redundant.,5744
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and RADIUS RATIO are redundant.,5745
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and RADIUS RATIO are redundant.,5746
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and RADIUS RATIO are redundant.,5747
vehicle_correlation_heatmap.png,Variables target and RADIUS RATIO are redundant.,5748
vehicle_correlation_heatmap.png,Variables COMPACTNESS and PR AXIS ASPECT RATIO are redundant.,5749
vehicle_correlation_heatmap.png,Variables CIRCULARITY and PR AXIS ASPECT RATIO are redundant.,5750
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and PR AXIS ASPECT RATIO are redundant.,5751
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and PR AXIS ASPECT RATIO are redundant.,5752
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and PR AXIS ASPECT RATIO are redundant.,5753
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and PR AXIS ASPECT RATIO are redundant.,5754
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and PR AXIS ASPECT RATIO are redundant.,5755
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and PR AXIS ASPECT RATIO are redundant.,5756
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and PR AXIS ASPECT RATIO are redundant.,5757
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and PR AXIS ASPECT RATIO are redundant.,5758
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and PR AXIS ASPECT RATIO are redundant.,5759
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and PR AXIS ASPECT RATIO are redundant.,5760
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and PR AXIS ASPECT RATIO are redundant.,5761
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and PR AXIS ASPECT RATIO are redundant.,5762
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and PR AXIS ASPECT RATIO are redundant.,5763
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and PR AXIS ASPECT RATIO are redundant.,5764
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and PR AXIS ASPECT RATIO are redundant.,5765
vehicle_correlation_heatmap.png,Variables target and PR AXIS ASPECT RATIO are redundant.,5766
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAX LENGTH ASPECT RATIO are redundant.,5767
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant.,5768
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant.,5769
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAX LENGTH ASPECT RATIO are redundant.,5770
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant.,5771
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAX LENGTH ASPECT RATIO are redundant.,5772
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAX LENGTH ASPECT RATIO are redundant.,5773
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAX LENGTH ASPECT RATIO are redundant.,5774
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAX LENGTH ASPECT RATIO are redundant.,5775
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MAX LENGTH ASPECT RATIO are redundant.,5776
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAX LENGTH ASPECT RATIO are redundant.,5777
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAX LENGTH ASPECT RATIO are redundant.,5778
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MAX LENGTH ASPECT RATIO are redundant.,5779
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAX LENGTH ASPECT RATIO are redundant.,5780
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAX LENGTH ASPECT RATIO are redundant.,5781
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MAX LENGTH ASPECT RATIO are redundant.,5782
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAX LENGTH ASPECT RATIO are redundant.,5783
vehicle_correlation_heatmap.png,Variables target and MAX LENGTH ASPECT RATIO are redundant.,5784
vehicle_correlation_heatmap.png,Variables COMPACTNESS and SCATTER RATIO are redundant.,5785
vehicle_correlation_heatmap.png,Variables CIRCULARITY and SCATTER RATIO are redundant.,5786
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and SCATTER RATIO are redundant.,5787
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and SCATTER RATIO are redundant.,5788
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and SCATTER RATIO are redundant.,5789
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and SCATTER RATIO are redundant.,5790
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and SCATTER RATIO are redundant.,5791
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and SCATTER RATIO are redundant.,5792
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and SCATTER RATIO are redundant.,5793
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and SCATTER RATIO are redundant.,5794
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and SCATTER RATIO are redundant.,5795
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and SCATTER RATIO are redundant.,5796
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and SCATTER RATIO are redundant.,5797
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and SCATTER RATIO are redundant.,5798
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and SCATTER RATIO are redundant.,5799
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and SCATTER RATIO are redundant.,5800
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and SCATTER RATIO are redundant.,5801
vehicle_correlation_heatmap.png,Variables target and SCATTER RATIO are redundant.,5802
vehicle_correlation_heatmap.png,Variables COMPACTNESS and ELONGATEDNESS are redundant.,5803
vehicle_correlation_heatmap.png,Variables CIRCULARITY and ELONGATEDNESS are redundant.,5804
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and ELONGATEDNESS are redundant.,5805
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and ELONGATEDNESS are redundant.,5806
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and ELONGATEDNESS are redundant.,5807
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant.,5808
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and ELONGATEDNESS are redundant.,5809
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and ELONGATEDNESS are redundant.,5810
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and ELONGATEDNESS are redundant.,5811
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and ELONGATEDNESS are redundant.,5812
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and ELONGATEDNESS are redundant.,5813
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and ELONGATEDNESS are redundant.,5814
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and ELONGATEDNESS are redundant.,5815
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and ELONGATEDNESS are redundant.,5816
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and ELONGATEDNESS are redundant.,5817
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and ELONGATEDNESS are redundant.,5818
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and ELONGATEDNESS are redundant.,5819
vehicle_correlation_heatmap.png,Variables target and ELONGATEDNESS are redundant.,5820
vehicle_correlation_heatmap.png,Variables COMPACTNESS and PR AXISRECTANGULAR are redundant.,5821
vehicle_correlation_heatmap.png,Variables CIRCULARITY and PR AXISRECTANGULAR are redundant.,5822
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR are redundant.,5823
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant.,5824
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and PR AXISRECTANGULAR are redundant.,5825
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR are redundant.,5826
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and PR AXISRECTANGULAR are redundant.,5827
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant.,5828
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and PR AXISRECTANGULAR are redundant.,5829
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and PR AXISRECTANGULAR are redundant.,5830
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and PR AXISRECTANGULAR are redundant.,5831
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and PR AXISRECTANGULAR are redundant.,5832
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and PR AXISRECTANGULAR are redundant.,5833
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and PR AXISRECTANGULAR are redundant.,5834
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and PR AXISRECTANGULAR are redundant.,5835
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and PR AXISRECTANGULAR are redundant.,5836
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and PR AXISRECTANGULAR are redundant.,5837
vehicle_correlation_heatmap.png,Variables target and PR AXISRECTANGULAR are redundant.,5838
vehicle_correlation_heatmap.png,Variables COMPACTNESS and LENGTHRECTANGULAR are redundant.,5839
vehicle_correlation_heatmap.png,Variables CIRCULARITY and LENGTHRECTANGULAR are redundant.,5840
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and LENGTHRECTANGULAR are redundant.,5841
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and LENGTHRECTANGULAR are redundant.,5842
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR are redundant.,5843
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR are redundant.,5844
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and LENGTHRECTANGULAR are redundant.,5845
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant.,5846
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and LENGTHRECTANGULAR are redundant.,5847
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and LENGTHRECTANGULAR are redundant.,5848
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and LENGTHRECTANGULAR are redundant.,5849
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and LENGTHRECTANGULAR are redundant.,5850
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and LENGTHRECTANGULAR are redundant.,5851
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and LENGTHRECTANGULAR are redundant.,5852
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and LENGTHRECTANGULAR are redundant.,5853
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and LENGTHRECTANGULAR are redundant.,5854
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and LENGTHRECTANGULAR are redundant.,5855
vehicle_correlation_heatmap.png,Variables target and LENGTHRECTANGULAR are redundant.,5856
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAJORVARIANCE are redundant.,5857
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAJORVARIANCE are redundant.,5858
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAJORVARIANCE are redundant.,5859
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAJORVARIANCE are redundant.,5860
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAJORVARIANCE are redundant.,5861
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MAJORVARIANCE are redundant.,5862
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAJORVARIANCE are redundant.,5863
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAJORVARIANCE are redundant.,5864
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORVARIANCE are redundant.,5865
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAJORVARIANCE are redundant.,5866
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORVARIANCE are redundant.,5867
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAJORVARIANCE are redundant.,5868
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MAJORVARIANCE are redundant.,5869
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAJORVARIANCE are redundant.,5870
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAJORVARIANCE are redundant.,5871
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MAJORVARIANCE are redundant.,5872
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAJORVARIANCE are redundant.,5873
vehicle_correlation_heatmap.png,Variables target and MAJORVARIANCE are redundant.,5874
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MINORVARIANCE are redundant.,5875
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MINORVARIANCE are redundant.,5876
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MINORVARIANCE are redundant.,5877
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MINORVARIANCE are redundant.,5878
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MINORVARIANCE are redundant.,5879
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE are redundant.,5880
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MINORVARIANCE are redundant.,5881
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORVARIANCE are redundant.,5882
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MINORVARIANCE are redundant.,5883
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MINORVARIANCE are redundant.,5884
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MINORVARIANCE are redundant.,5885
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MINORVARIANCE are redundant.,5886
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MINORVARIANCE are redundant.,5887
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MINORVARIANCE are redundant.,5888
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MINORVARIANCE are redundant.,5889
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MINORVARIANCE are redundant.,5890
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MINORVARIANCE are redundant.,5891
vehicle_correlation_heatmap.png,Variables target and MINORVARIANCE are redundant.,5892
vehicle_correlation_heatmap.png,Variables COMPACTNESS and GYRATIONRADIUS are redundant.,5893
vehicle_correlation_heatmap.png,Variables CIRCULARITY and GYRATIONRADIUS are redundant.,5894
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant.,5895
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and GYRATIONRADIUS are redundant.,5896
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS are redundant.,5897
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant.,5898
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and GYRATIONRADIUS are redundant.,5899
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and GYRATIONRADIUS are redundant.,5900
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and GYRATIONRADIUS are redundant.,5901
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and GYRATIONRADIUS are redundant.,5902
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and GYRATIONRADIUS are redundant.,5903
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and GYRATIONRADIUS are redundant.,5904
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and GYRATIONRADIUS are redundant.,5905
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and GYRATIONRADIUS are redundant.,5906
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and GYRATIONRADIUS are redundant.,5907
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and GYRATIONRADIUS are redundant.,5908
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and GYRATIONRADIUS are redundant.,5909
vehicle_correlation_heatmap.png,Variables target and GYRATIONRADIUS are redundant.,5910
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAJORSKEWNESS are redundant.,5911
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAJORSKEWNESS are redundant.,5912
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAJORSKEWNESS are redundant.,5913
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAJORSKEWNESS are redundant.,5914
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant.,5915
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MAJORSKEWNESS are redundant.,5916
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAJORSKEWNESS are redundant.,5917
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAJORSKEWNESS are redundant.,5918
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORSKEWNESS are redundant.,5919
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAJORSKEWNESS are redundant.,5920
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MAJORSKEWNESS are redundant.,5921
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORSKEWNESS are redundant.,5922
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant.,5923
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAJORSKEWNESS are redundant.,5924
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAJORSKEWNESS are redundant.,5925
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MAJORSKEWNESS are redundant.,5926
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAJORSKEWNESS are redundant.,5927
vehicle_correlation_heatmap.png,Variables target and MAJORSKEWNESS are redundant.,5928
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MINORSKEWNESS are redundant.,5929
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MINORSKEWNESS are redundant.,5930
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MINORSKEWNESS are redundant.,5931
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MINORSKEWNESS are redundant.,5932
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MINORSKEWNESS are redundant.,5933
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant.,5934
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MINORSKEWNESS are redundant.,5935
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORSKEWNESS are redundant.,5936
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MINORSKEWNESS are redundant.,5937
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MINORSKEWNESS are redundant.,5938
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MINORSKEWNESS are redundant.,5939
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MINORSKEWNESS are redundant.,5940
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MINORSKEWNESS are redundant.,5941
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MINORSKEWNESS are redundant.,5942
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MINORSKEWNESS are redundant.,5943
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MINORSKEWNESS are redundant.,5944
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MINORSKEWNESS are redundant.,5945
vehicle_correlation_heatmap.png,Variables target and MINORSKEWNESS are redundant.,5946
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MINORKURTOSIS are redundant.,5947
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MINORKURTOSIS are redundant.,5948
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MINORKURTOSIS are redundant.,5949
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MINORKURTOSIS are redundant.,5950
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MINORKURTOSIS are redundant.,5951
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MINORKURTOSIS are redundant.,5952
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MINORKURTOSIS are redundant.,5953
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORKURTOSIS are redundant.,5954
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MINORKURTOSIS are redundant.,5955
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MINORKURTOSIS are redundant.,5956
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MINORKURTOSIS are redundant.,5957
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MINORKURTOSIS are redundant.,5958
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MINORKURTOSIS are redundant.,5959
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MINORKURTOSIS are redundant.,5960
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MINORKURTOSIS are redundant.,5961
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and MINORKURTOSIS are redundant.,5962
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MINORKURTOSIS are redundant.,5963
vehicle_correlation_heatmap.png,Variables target and MINORKURTOSIS are redundant.,5964
vehicle_correlation_heatmap.png,Variables COMPACTNESS and MAJORKURTOSIS are redundant.,5965
vehicle_correlation_heatmap.png,Variables CIRCULARITY and MAJORKURTOSIS are redundant.,5966
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAJORKURTOSIS are redundant.,5967
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and MAJORKURTOSIS are redundant.,5968
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS are redundant.,5969
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MAJORKURTOSIS are redundant.,5970
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAJORKURTOSIS are redundant.,5971
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAJORKURTOSIS are redundant.,5972
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORKURTOSIS are redundant.,5973
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and MAJORKURTOSIS are redundant.,5974
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MAJORKURTOSIS are redundant.,5975
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORKURTOSIS are redundant.,5976
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and MAJORKURTOSIS are redundant.,5977
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MAJORKURTOSIS are redundant.,5978
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAJORKURTOSIS are redundant.,5979
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and MAJORKURTOSIS are redundant.,5980
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAJORKURTOSIS are redundant.,5981
vehicle_correlation_heatmap.png,Variables target and MAJORKURTOSIS are redundant.,5982
vehicle_correlation_heatmap.png,Variables COMPACTNESS and HOLLOWS RATIO are redundant.,5983
vehicle_correlation_heatmap.png,Variables CIRCULARITY and HOLLOWS RATIO are redundant.,5984
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant.,5985
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and HOLLOWS RATIO are redundant.,5986
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant.,5987
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO are redundant.,5988
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and HOLLOWS RATIO are redundant.,5989
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and HOLLOWS RATIO are redundant.,5990
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and HOLLOWS RATIO are redundant.,5991
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and HOLLOWS RATIO are redundant.,5992
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and HOLLOWS RATIO are redundant.,5993
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and HOLLOWS RATIO are redundant.,5994
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and HOLLOWS RATIO are redundant.,5995
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and HOLLOWS RATIO are redundant.,5996
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and HOLLOWS RATIO are redundant.,5997
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and HOLLOWS RATIO are redundant.,5998
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and HOLLOWS RATIO are redundant.,5999
vehicle_correlation_heatmap.png,Variables target and HOLLOWS RATIO are redundant.,6000
vehicle_correlation_heatmap.png,Variables COMPACTNESS and target are redundant.,6001
vehicle_correlation_heatmap.png,Variables CIRCULARITY and target are redundant.,6002
vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and target are redundant.,6003
vehicle_correlation_heatmap.png,Variables RADIUS RATIO and target are redundant.,6004
vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and target are redundant.,6005
vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and target are redundant.,6006
vehicle_correlation_heatmap.png,Variables SCATTER RATIO and target are redundant.,6007
vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and target are redundant.,6008
vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and target are redundant.,6009
vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and target are redundant.,6010
vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and target are redundant.,6011
vehicle_correlation_heatmap.png,Variables MINORVARIANCE and target are redundant.,6012
vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and target are redundant.,6013
vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and target are redundant.,6014
vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and target are redundant.,6015
vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and target are redundant.,6016
vehicle_correlation_heatmap.png,Variables MAJORKURTOSIS and target are redundant.,6017
vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and target are redundant.,6018
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MAJORSKEWNESS.",6019
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and GYRATIONRADIUS.",6020
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MAJORSKEWNESS and HOLLOWS RATIO.",6021
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6022
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORVARIANCE.",6023
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORKURTOSIS.",6024
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and GYRATIONRADIUS.",6025
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORKURTOSIS.",6026
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6027
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORVARIANCE.",6028
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6029
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORKURTOSIS.",6030
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MAJORKURTOSIS.",6031
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORKURTOSIS.",6032
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and GYRATIONRADIUS.",6033
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MAJORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and ELONGATEDNESS.",6034
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6035
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORSKEWNESS.",6036
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and CIRCULARITY are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and LENGTHRECTANGULAR.",6037
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MAJORSKEWNESS.",6038
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR.",6039
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6040
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR.",6041
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and HOLLOWS RATIO.",6042
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair MINORSKEWNESS and MAJORKURTOSIS.",6043
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORVARIANCE are redundant, but we can’t say the same for the pair RADIUS RATIO and SCATTER RATIO.",6044
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MAJORSKEWNESS and HOLLOWS RATIO.",6045
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6046
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and LENGTHRECTANGULAR.",6047
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and SCATTER RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORKURTOSIS.",6048
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair SCATTER RATIO and LENGTHRECTANGULAR.",6049
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and HOLLOWS RATIO.",6050
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and MAJORSKEWNESS.",6051
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORVARIANCE.",6052
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORVARIANCE.",6053
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORSKEWNESS.",6054
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR.",6055
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORVARIANCE.",6056
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORVARIANCE.",6057
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MINORKURTOSIS.",6058
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and MAX LENGTH ASPECT RATIO.",6059
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORVARIANCE.",6060
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORKURTOSIS.",6061
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and GYRATIONRADIUS.",6062
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORKURTOSIS.",6063
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORVARIANCE.",6064
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORSKEWNESS.",6065
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MINORSKEWNESS.",6066
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORVARIANCE.",6067
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORKURTOSIS.",6068
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and HOLLOWS RATIO.",6069
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORSKEWNESS.",6070
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and ELONGATEDNESS.",6071
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6072
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6073
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORSKEWNESS.",6074
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MINORKURTOSIS.",6075
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and GYRATIONRADIUS.",6076
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and SCATTER RATIO.",6077
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and HOLLOWS RATIO.",6078
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6079
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORSKEWNESS.",6080
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6081
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORKURTOSIS.",6082
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORVARIANCE.",6083
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and SCATTER RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXISRECTANGULAR.",6084
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair RADIUS RATIO and LENGTHRECTANGULAR.",6085
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORKURTOSIS.",6086
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6087
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORSKEWNESS.",6088
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6089
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and HOLLOWS RATIO.",6090
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORKURTOSIS.",6091
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6092
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR.",6093
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and LENGTHRECTANGULAR.",6094
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORKURTOSIS.",6095
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and LENGTHRECTANGULAR.",6096
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO.",6097
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and GYRATIONRADIUS.",6098
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6099
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORKURTOSIS.",6100
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORVARIANCE are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORKURTOSIS.",6101
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and HOLLOWS RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORVARIANCE.",6102
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORKURTOSIS.",6103
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and ELONGATEDNESS.",6104
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6105
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and MAX LENGTH ASPECT RATIO.",6106
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORVARIANCE are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6107
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair MINORVARIANCE and GYRATIONRADIUS.",6108
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and CIRCULARITY are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORSKEWNESS.",6109
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORSKEWNESS.",6110
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORVARIANCE.",6111
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair SCATTER RATIO and GYRATIONRADIUS.",6112
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and ELONGATEDNESS.",6113
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and CIRCULARITY are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and ELONGATEDNESS.",6114
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORKURTOSIS.",6115
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORKURTOSIS.",6116
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and HOLLOWS RATIO.",6117
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MAJORSKEWNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORVARIANCE.",6118
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and HOLLOWS RATIO.",6119
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and GYRATIONRADIUS.",6120
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORSKEWNESS.",6121
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORVARIANCE.",6122
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORVARIANCE.",6123
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and GYRATIONRADIUS.",6124
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and LENGTHRECTANGULAR.",6125
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MINORVARIANCE and GYRATIONRADIUS.",6126
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORSKEWNESS.",6127
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and SCATTER RATIO.",6128
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and HOLLOWS RATIO.",6129
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORVARIANCE.",6130
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORKURTOSIS.",6131
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6132
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and GYRATIONRADIUS.",6133
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORKURTOSIS.",6134
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORSKEWNESS.",6135
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6136
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORVARIANCE.",6137
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORVARIANCE.",6138
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORSKEWNESS.",6139
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and PR AXISRECTANGULAR.",6140
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and ELONGATEDNESS.",6141
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORKURTOSIS.",6142
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORKURTOSIS.",6143
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORSKEWNESS.",6144
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORVARIANCE.",6145
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and GYRATIONRADIUS are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORKURTOSIS.",6146
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and HOLLOWS RATIO.",6147
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORVARIANCE are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and GYRATIONRADIUS.",6148
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORKURTOSIS.",6149
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORSKEWNESS.",6150
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MINORKURTOSIS and HOLLOWS RATIO.",6151
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair COMPACTNESS and HOLLOWS RATIO.",6152
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORKURTOSIS.",6153
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and HOLLOWS RATIO.",6154
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6155
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORSKEWNESS.",6156
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6157
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORKURTOSIS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORKURTOSIS.",6158
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORSKEWNESS.",6159
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and MAX LENGTH ASPECT RATIO.",6160
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and GYRATIONRADIUS.",6161
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair COMPACTNESS and RADIUS RATIO.",6162
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORKURTOSIS.",6163
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORSKEWNESS.",6164
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MAX LENGTH ASPECT RATIO.",6165
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORKURTOSIS.",6166
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORSKEWNESS.",6167
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORSKEWNESS.",6168
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and GYRATIONRADIUS.",6169
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair CIRCULARITY and ELONGATEDNESS.",6170
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6171
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORKURTOSIS.",6172
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and HOLLOWS RATIO.",6173
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORSKEWNESS.",6174
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORVARIANCE.",6175
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORVARIANCE.",6176
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6177
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and ELONGATEDNESS.",6178
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and RADIUS RATIO.",6179
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORSKEWNESS.",6180
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORKURTOSIS.",6181
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6182
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORKURTOSIS.",6183
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORVARIANCE.",6184
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6185
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MAJORKURTOSIS and HOLLOWS RATIO.",6186
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORKURTOSIS.",6187
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and GYRATIONRADIUS.",6188
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MINORKURTOSIS and MAJORKURTOSIS.",6189
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAJORSKEWNESS and HOLLOWS RATIO.",6190
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORSKEWNESS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORVARIANCE.",6191
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and SCATTER RATIO.",6192
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and SCATTER RATIO.",6193
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORVARIANCE.",6194
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6195
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and LENGTHRECTANGULAR.",6196
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORVARIANCE.",6197
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORKURTOSIS.",6198
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and CIRCULARITY are redundant, but we can’t say the same for the pair MINORSKEWNESS and MAJORKURTOSIS.",6199
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORVARIANCE.",6200
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORKURTOSIS.",6201
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO.",6202
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORSKEWNESS.",6203
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and LENGTHRECTANGULAR.",6204
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORSKEWNESS.",6205
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6206
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORSKEWNESS.",6207
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXIS ASPECT RATIO.",6208
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORVARIANCE.",6209
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and GYRATIONRADIUS.",6210
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR.",6211
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and RADIUS RATIO.",6212
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and GYRATIONRADIUS.",6213
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MAX LENGTH ASPECT RATIO.",6214
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAJORVARIANCE and HOLLOWS RATIO.",6215
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and SCATTER RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORKURTOSIS.",6216
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXIS ASPECT RATIO.",6217
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORSKEWNESS.",6218
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and PR AXISRECTANGULAR.",6219
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORVARIANCE are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORKURTOSIS.",6220
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORVARIANCE.",6221
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and GYRATIONRADIUS.",6222
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MINORVARIANCE and GYRATIONRADIUS.",6223
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORSKEWNESS.",6224
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and GYRATIONRADIUS.",6225
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXIS ASPECT RATIO.",6226
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MINORVARIANCE.",6227
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORVARIANCE.",6228
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and SCATTER RATIO.",6229
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAJORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and ELONGATEDNESS.",6230
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and HOLLOWS RATIO.",6231
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORKURTOSIS.",6232
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and GYRATIONRADIUS.",6233
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and ELONGATEDNESS.",6234
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and LENGTHRECTANGULAR.",6235
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAJORVARIANCE and HOLLOWS RATIO.",6236
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and LENGTHRECTANGULAR.",6237
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORKURTOSIS.",6238
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and ELONGATEDNESS.",6239
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and HOLLOWS RATIO.",6240
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and GYRATIONRADIUS.",6241
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair MAJORVARIANCE and HOLLOWS RATIO.",6242
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORVARIANCE.",6243
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6244
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORVARIANCE.",6245
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and GYRATIONRADIUS.",6246
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and PR AXIS ASPECT RATIO.",6247
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORVARIANCE.",6248
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORKURTOSIS.",6249
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORKURTOSIS.",6250
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MINORKURTOSIS.",6251
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORKURTOSIS.",6252
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORSKEWNESS.",6253
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXISRECTANGULAR.",6254
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and GYRATIONRADIUS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORKURTOSIS.",6255
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and HOLLOWS RATIO.",6256
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and HOLLOWS RATIO.",6257
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6258
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6259
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORKURTOSIS.",6260
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MAJORKURTOSIS and HOLLOWS RATIO.",6261
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORSKEWNESS.",6262
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORKURTOSIS.",6263
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and LENGTHRECTANGULAR.",6264
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and HOLLOWS RATIO.",6265
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and HOLLOWS RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORKURTOSIS.",6266
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and HOLLOWS RATIO.",6267
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair GYRATIONRADIUS and HOLLOWS RATIO.",6268
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6269
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and PR AXISRECTANGULAR.",6270
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and LENGTHRECTANGULAR.",6271
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6272
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAJORVARIANCE and MAJORSKEWNESS.",6273
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and LENGTHRECTANGULAR.",6274
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6275
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORSKEWNESS.",6276
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and RADIUS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXISRECTANGULAR.",6277
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORVARIANCE are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO.",6278
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXISRECTANGULAR.",6279
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MINORSKEWNESS.",6280
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and GYRATIONRADIUS.",6281
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and GYRATIONRADIUS.",6282
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and CIRCULARITY are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and GYRATIONRADIUS.",6283
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MAJORKURTOSIS.",6284
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORVARIANCE are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORKURTOSIS.",6285
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair MAJORKURTOSIS and HOLLOWS RATIO.",6286
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORVARIANCE.",6287
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6288
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORKURTOSIS.",6289
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXISRECTANGULAR.",6290
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORSKEWNESS.",6291
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXIS ASPECT RATIO.",6292
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXIS ASPECT RATIO.",6293
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6294
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORSKEWNESS.",6295
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MINORSKEWNESS and HOLLOWS RATIO.",6296
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and HOLLOWS RATIO.",6297
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and ELONGATEDNESS.",6298
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORKURTOSIS.",6299
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MINORKURTOSIS and MAJORKURTOSIS.",6300
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and LENGTHRECTANGULAR.",6301
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and ELONGATEDNESS.",6302
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and MAX LENGTH ASPECT RATIO.",6303
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and HOLLOWS RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and MAX LENGTH ASPECT RATIO.",6304
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6305
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and RADIUS RATIO.",6306
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and GYRATIONRADIUS.",6307
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORSKEWNESS.",6308
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and GYRATIONRADIUS.",6309
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORSKEWNESS.",6310
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and ELONGATEDNESS.",6311
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and GYRATIONRADIUS.",6312
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORVARIANCE.",6313
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORSKEWNESS.",6314
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and GYRATIONRADIUS.",6315
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and PR AXIS ASPECT RATIO.",6316
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6317
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORVARIANCE.",6318
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORVARIANCE.",6319
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORVARIANCE.",6320
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair ELONGATEDNESS and PR AXISRECTANGULAR.",6321
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6322
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and SCATTER RATIO.",6323
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORKURTOSIS.",6324
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and GYRATIONRADIUS.",6325
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and GYRATIONRADIUS.",6326
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAX LENGTH ASPECT RATIO.",6327
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and MAX LENGTH ASPECT RATIO.",6328
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MAJORVARIANCE are redundant, but we can’t say the same for the pair MAJORKURTOSIS and HOLLOWS RATIO.",6329
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and SCATTER RATIO are redundant, but we can’t say the same for the pair MINORVARIANCE and MINORSKEWNESS.",6330
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6331
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and GYRATIONRADIUS.",6332
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6333
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORVARIANCE.",6334
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and HOLLOWS RATIO.",6335
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MINORSKEWNESS.",6336
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORVARIANCE are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and PR AXISRECTANGULAR.",6337
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORVARIANCE.",6338
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORSKEWNESS.",6339
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and CIRCULARITY.",6340
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and SCATTER RATIO.",6341
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORSKEWNESS.",6342
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and SCATTER RATIO.",6343
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair GYRATIONRADIUS and HOLLOWS RATIO.",6344
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORKURTOSIS.",6345
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and MINORVARIANCE.",6346
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MINORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6347
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORVARIANCE.",6348
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORVARIANCE.",6349
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MAJORVARIANCE and GYRATIONRADIUS.",6350
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORKURTOSIS.",6351
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORVARIANCE.",6352
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and SCATTER RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6353
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORSKEWNESS.",6354
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and HOLLOWS RATIO.",6355
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6356
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and SCATTER RATIO.",6357
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and DISTANCE CIRCULARITY.",6358
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6359
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6360
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAJORVARIANCE and GYRATIONRADIUS.",6361
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORSKEWNESS.",6362
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORKURTOSIS.",6363
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORKURTOSIS.",6364
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and HOLLOWS RATIO.",6365
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORKURTOSIS.",6366
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR.",6367
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORKURTOSIS.",6368
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORVARIANCE.",6369
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and SCATTER RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORVARIANCE.",6370
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORVARIANCE.",6371
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair SCATTER RATIO and PR AXISRECTANGULAR.",6372
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORVARIANCE.",6373
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6374
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and CIRCULARITY.",6375
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and SCATTER RATIO.",6376
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and RADIUS RATIO.",6377
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and ELONGATEDNESS.",6378
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and SCATTER RATIO.",6379
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR.",6380
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and GYRATIONRADIUS.",6381
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORVARIANCE.",6382
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and GYRATIONRADIUS.",6383
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6384
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORVARIANCE.",6385
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair ELONGATEDNESS and HOLLOWS RATIO.",6386
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and LENGTHRECTANGULAR.",6387
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORKURTOSIS.",6388
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and HOLLOWS RATIO.",6389
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and RADIUS RATIO.",6390
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and SCATTER RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORKURTOSIS.",6391
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXISRECTANGULAR.",6392
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXISRECTANGULAR.",6393
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORSKEWNESS.",6394
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORVARIANCE are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and HOLLOWS RATIO.",6395
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORVARIANCE.",6396
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORSKEWNESS.",6397
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORVARIANCE.",6398
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MINORVARIANCE and GYRATIONRADIUS.",6399
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORVARIANCE.",6400
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORKURTOSIS.",6401
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MINORVARIANCE and HOLLOWS RATIO.",6402
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MINORSKEWNESS and MAJORKURTOSIS.",6403
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORSKEWNESS.",6404
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and HOLLOWS RATIO.",6405
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and LENGTHRECTANGULAR.",6406
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and SCATTER RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and PR AXISRECTANGULAR.",6407
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORSKEWNESS.",6408
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORVARIANCE are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6409
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORVARIANCE.",6410
vehicle_correlation_heatmap.png,"Variables MAJORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6411
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORKURTOSIS.",6412
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORSKEWNESS.",6413
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MAJORSKEWNESS and HOLLOWS RATIO.",6414
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORKURTOSIS.",6415
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and LENGTHRECTANGULAR.",6416
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and DISTANCE CIRCULARITY.",6417
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and CIRCULARITY are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORKURTOSIS.",6418
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair ELONGATEDNESS and HOLLOWS RATIO.",6419
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORVARIANCE.",6420
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORVARIANCE are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORSKEWNESS.",6421
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORKURTOSIS.",6422
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORVARIANCE.",6423
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MINORVARIANCE and MINORSKEWNESS.",6424
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAJORVARIANCE are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MINORKURTOSIS.",6425
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORSKEWNESS.",6426
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORVARIANCE.",6427
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORKURTOSIS.",6428
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and MAX LENGTH ASPECT RATIO.",6429
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORSKEWNESS.",6430
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6431
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6432
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6433
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair COMPACTNESS and MINORSKEWNESS.",6434
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MINORSKEWNESS are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORSKEWNESS.",6435
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORVARIANCE.",6436
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORSKEWNESS.",6437
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6438
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORKURTOSIS.",6439
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and RADIUS RATIO.",6440
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORKURTOSIS.",6441
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR.",6442
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6443
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORVARIANCE are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and PR AXISRECTANGULAR.",6444
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORSKEWNESS.",6445
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6446
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and MINORKURTOSIS.",6447
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MAX LENGTH ASPECT RATIO.",6448
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6449
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORSKEWNESS.",6450
vehicle_correlation_heatmap.png,"Variables MAJORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and GYRATIONRADIUS.",6451
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and GYRATIONRADIUS.",6452
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORSKEWNESS.",6453
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO.",6454
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR.",6455
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and HOLLOWS RATIO.",6456
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORSKEWNESS.",6457
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and HOLLOWS RATIO.",6458
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MAJORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORSKEWNESS.",6459
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MAJORKURTOSIS.",6460
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and PR AXIS ASPECT RATIO.",6461
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXIS ASPECT RATIO.",6462
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair MINORKURTOSIS and HOLLOWS RATIO.",6463
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MAJORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORKURTOSIS.",6464
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6465
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and ELONGATEDNESS.",6466
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and PR AXISRECTANGULAR.",6467
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6468
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and LENGTHRECTANGULAR.",6469
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORSKEWNESS.",6470
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORVARIANCE.",6471
vehicle_correlation_heatmap.png,"Variables MAJORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6472
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6473
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR.",6474
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORVARIANCE.",6475
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MINORVARIANCE.",6476
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORKURTOSIS.",6477
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and LENGTHRECTANGULAR.",6478
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORSKEWNESS.",6479
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6480
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORVARIANCE are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORSKEWNESS.",6481
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and HOLLOWS RATIO.",6482
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6483
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and LENGTHRECTANGULAR.",6484
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORVARIANCE.",6485
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORSKEWNESS.",6486
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and LENGTHRECTANGULAR.",6487
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair MINORVARIANCE and MINORSKEWNESS.",6488
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6489
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORKURTOSIS.",6490
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MINORSKEWNESS and MAJORKURTOSIS.",6491
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and GYRATIONRADIUS.",6492
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and SCATTER RATIO.",6493
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6494
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6495
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair MINORSKEWNESS and MAJORKURTOSIS.",6496
vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and GYRATIONRADIUS.",6497
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MINORVARIANCE and GYRATIONRADIUS.",6498
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAJORVARIANCE are redundant, but we can’t say the same for the pair SCATTER RATIO and PR AXISRECTANGULAR.",6499
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and HOLLOWS RATIO.",6500
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and GYRATIONRADIUS.",6501
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORKURTOSIS.",6502
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6503
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and LENGTHRECTANGULAR.",6504
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORVARIANCE.",6505
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair SCATTER RATIO and ELONGATEDNESS.",6506
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair RADIUS RATIO and ELONGATEDNESS.",6507
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORKURTOSIS.",6508
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and RADIUS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR.",6509
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORVARIANCE are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORKURTOSIS.",6510
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6511
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and RADIUS RATIO.",6512
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and ELONGATEDNESS.",6513
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and MAJORSKEWNESS.",6514
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6515
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and PR AXISRECTANGULAR.",6516
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair COMPACTNESS and DISTANCE CIRCULARITY.",6517
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORSKEWNESS.",6518
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MINORSKEWNESS.",6519
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and RADIUS RATIO are redundant, but we can’t say the same for the pair MAJORSKEWNESS and HOLLOWS RATIO.",6520
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and SCATTER RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORKURTOSIS.",6521
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6522
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and MAJORKURTOSIS.",6523
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6524
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORSKEWNESS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORVARIANCE.",6525
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORSKEWNESS.",6526
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and HOLLOWS RATIO.",6527
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and RADIUS RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORKURTOSIS.",6528
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MINORSKEWNESS.",6529
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORVARIANCE.",6530
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MAX LENGTH ASPECT RATIO.",6531
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORSKEWNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and SCATTER RATIO.",6532
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXISRECTANGULAR.",6533
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORKURTOSIS.",6534
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MINORVARIANCE.",6535
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and CIRCULARITY are redundant, but we can’t say the same for the pair RADIUS RATIO and GYRATIONRADIUS.",6536
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORVARIANCE.",6537
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MAJORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6538
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORVARIANCE.",6539
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and RADIUS RATIO.",6540
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORSKEWNESS.",6541
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORSKEWNESS.",6542
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and HOLLOWS RATIO.",6543
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORSKEWNESS.",6544
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and SCATTER RATIO.",6545
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORVARIANCE.",6546
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MAJORKURTOSIS.",6547
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORVARIANCE.",6548
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORSKEWNESS.",6549
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAJORVARIANCE and HOLLOWS RATIO.",6550
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and HOLLOWS RATIO.",6551
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORVARIANCE.",6552
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORKURTOSIS.",6553
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and SCATTER RATIO.",6554
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and ELONGATEDNESS.",6555
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORVARIANCE.",6556
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORVARIANCE.",6557
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and RADIUS RATIO.",6558
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and GYRATIONRADIUS.",6559
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORSKEWNESS.",6560
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6561
vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and LENGTHRECTANGULAR.",6562
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and HOLLOWS RATIO.",6563
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and LENGTHRECTANGULAR.",6564
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and SCATTER RATIO.",6565
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and LENGTHRECTANGULAR.",6566
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair MAJORSKEWNESS and HOLLOWS RATIO.",6567
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXIS ASPECT RATIO.",6568
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORSKEWNESS.",6569
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair SCATTER RATIO and PR AXISRECTANGULAR.",6570
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and PR AXISRECTANGULAR.",6571
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and HOLLOWS RATIO.",6572
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORVARIANCE.",6573
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORKURTOSIS.",6574
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6575
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORSKEWNESS.",6576
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORKURTOSIS.",6577
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MINORSKEWNESS.",6578
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORVARIANCE.",6579
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORSKEWNESS.",6580
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MINORKURTOSIS and HOLLOWS RATIO.",6581
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6582
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair MAJORSKEWNESS and MINORKURTOSIS.",6583
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and SCATTER RATIO are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6584
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORVARIANCE are redundant, but we can’t say the same for the pair MINORKURTOSIS and HOLLOWS RATIO.",6585
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and LENGTHRECTANGULAR.",6586
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair GYRATIONRADIUS and MAJORKURTOSIS.",6587
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and HOLLOWS RATIO.",6588
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORSKEWNESS.",6589
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORKURTOSIS.",6590
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and LENGTHRECTANGULAR.",6591
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair COMPACTNESS and MINORSKEWNESS.",6592
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and PR AXISRECTANGULAR.",6593
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORKURTOSIS.",6594
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6595
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MINORSKEWNESS.",6596
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and DISTANCE CIRCULARITY.",6597
vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair ELONGATEDNESS and GYRATIONRADIUS.",6598
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair RADIUS RATIO and LENGTHRECTANGULAR.",6599
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and HOLLOWS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MAJORSKEWNESS.",6600
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair COMPACTNESS and MINORKURTOSIS.",6601
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair RADIUS RATIO and HOLLOWS RATIO.",6602
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORSKEWNESS.",6603
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and LENGTHRECTANGULAR.",6604
vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORKURTOSIS.",6605
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MINORVARIANCE.",6606
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORSKEWNESS.",6607
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and HOLLOWS RATIO.",6608
vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORKURTOSIS.",6609
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and ELONGATEDNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORVARIANCE.",6610
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and LENGTHRECTANGULAR.",6611
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and RADIUS RATIO.",6612
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and PR AXISRECTANGULAR.",6613
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and MAJORSKEWNESS.",6614
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORVARIANCE are redundant, but we can’t say the same for the pair COMPACTNESS and RADIUS RATIO.",6615
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORKURTOSIS.",6616
vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXIS ASPECT RATIO.",6617
vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and SCATTER RATIO.",6618
vehicle_correlation_heatmap.png,"Variables COMPACTNESS and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair RADIUS RATIO and LENGTHRECTANGULAR.",6619
vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAX LENGTH ASPECT RATIO.",6620
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair RADIUS RATIO and MINORVARIANCE.",6621
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and MAJORSKEWNESS.",6622
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MAJORKURTOSIS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORSKEWNESS.",6623
vehicle_correlation_heatmap.png,"Variables CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and LENGTHRECTANGULAR.",6624
vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MINORSKEWNESS.",6625
vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair MINORVARIANCE and MAJORSKEWNESS.",6626
vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MINORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORSKEWNESS.",6627
vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and GYRATIONRADIUS.",6628
vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MAJORSKEWNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORKURTOSIS.",6629
vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and HOLLOWS RATIO are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORSKEWNESS.",6630
vehicle_correlation_heatmap.png,The variable COMPACTNESS can be discarded without risking losing information.,6631
vehicle_correlation_heatmap.png,The variable CIRCULARITY can be discarded without risking losing information.,6632
vehicle_correlation_heatmap.png,The variable DISTANCE CIRCULARITY can be discarded without risking losing information.,6633
vehicle_correlation_heatmap.png,The variable RADIUS RATIO can be discarded without risking losing information.,6634
vehicle_correlation_heatmap.png,The variable PR AXIS ASPECT RATIO can be discarded without risking losing information.,6635
vehicle_correlation_heatmap.png,The variable MAX LENGTH ASPECT RATIO can be discarded without risking losing information.,6636
vehicle_correlation_heatmap.png,The variable SCATTER RATIO can be discarded without risking losing information.,6637
vehicle_correlation_heatmap.png,The variable ELONGATEDNESS can be discarded without risking losing information.,6638
vehicle_correlation_heatmap.png,The variable PR AXISRECTANGULAR can be discarded without risking losing information.,6639
vehicle_correlation_heatmap.png,The variable LENGTHRECTANGULAR can be discarded without risking losing information.,6640
vehicle_correlation_heatmap.png,The variable MAJORVARIANCE can be discarded without risking losing information.,6641
vehicle_correlation_heatmap.png,The variable MINORVARIANCE can be discarded without risking losing information.,6642
vehicle_correlation_heatmap.png,The variable GYRATIONRADIUS can be discarded without risking losing information.,6643
vehicle_correlation_heatmap.png,The variable MAJORSKEWNESS can be discarded without risking losing information.,6644
vehicle_correlation_heatmap.png,The variable MINORSKEWNESS can be discarded without risking losing information.,6645
vehicle_correlation_heatmap.png,The variable MINORKURTOSIS can be discarded without risking losing information.,6646
vehicle_correlation_heatmap.png,The variable MAJORKURTOSIS can be discarded without risking losing information.,6647
vehicle_correlation_heatmap.png,The variable HOLLOWS RATIO can be discarded without risking losing information.,6648
vehicle_correlation_heatmap.png,The variable target can be discarded without risking losing information.,6649
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or COMPACTNESS can be discarded without losing information.,6650
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or COMPACTNESS can be discarded without losing information.,6651
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or COMPACTNESS can be discarded without losing information.,6652
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or COMPACTNESS can be discarded without losing information.,6653
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or COMPACTNESS can be discarded without losing information.,6654
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or COMPACTNESS can be discarded without losing information.,6655
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or COMPACTNESS can be discarded without losing information.,6656
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or COMPACTNESS can be discarded without losing information.,6657
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or COMPACTNESS can be discarded without losing information.,6658
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or COMPACTNESS can be discarded without losing information.,6659
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or COMPACTNESS can be discarded without losing information.,6660
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or COMPACTNESS can be discarded without losing information.,6661
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or COMPACTNESS can be discarded without losing information.,6662
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or COMPACTNESS can be discarded without losing information.,6663
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or COMPACTNESS can be discarded without losing information.,6664
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or COMPACTNESS can be discarded without losing information.,6665
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or COMPACTNESS can be discarded without losing information.,6666
vehicle_correlation_heatmap.png,One of the variables target or COMPACTNESS can be discarded without losing information.,6667
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or CIRCULARITY can be discarded without losing information.,6668
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or CIRCULARITY can be discarded without losing information.,6669
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or CIRCULARITY can be discarded without losing information.,6670
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or CIRCULARITY can be discarded without losing information.,6671
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or CIRCULARITY can be discarded without losing information.,6672
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or CIRCULARITY can be discarded without losing information.,6673
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or CIRCULARITY can be discarded without losing information.,6674
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or CIRCULARITY can be discarded without losing information.,6675
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or CIRCULARITY can be discarded without losing information.,6676
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or CIRCULARITY can be discarded without losing information.,6677
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or CIRCULARITY can be discarded without losing information.,6678
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or CIRCULARITY can be discarded without losing information.,6679
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or CIRCULARITY can be discarded without losing information.,6680
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or CIRCULARITY can be discarded without losing information.,6681
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or CIRCULARITY can be discarded without losing information.,6682
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or CIRCULARITY can be discarded without losing information.,6683
vehicle_correlation_heatmap.png,One of the variables target or CIRCULARITY can be discarded without losing information.,6684
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6685
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or DISTANCE CIRCULARITY can be discarded without losing information.,6686
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or DISTANCE CIRCULARITY can be discarded without losing information.,6687
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or DISTANCE CIRCULARITY can be discarded without losing information.,6688
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or DISTANCE CIRCULARITY can be discarded without losing information.,6689
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or DISTANCE CIRCULARITY can be discarded without losing information.,6690
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6691
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or DISTANCE CIRCULARITY can be discarded without losing information.,6692
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or DISTANCE CIRCULARITY can be discarded without losing information.,6693
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or DISTANCE CIRCULARITY can be discarded without losing information.,6694
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or DISTANCE CIRCULARITY can be discarded without losing information.,6695
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or DISTANCE CIRCULARITY can be discarded without losing information.,6696
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6697
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6698
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or DISTANCE CIRCULARITY can be discarded without losing information.,6699
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or DISTANCE CIRCULARITY can be discarded without losing information.,6700
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or DISTANCE CIRCULARITY can be discarded without losing information.,6701
vehicle_correlation_heatmap.png,One of the variables target or DISTANCE CIRCULARITY can be discarded without losing information.,6702
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or RADIUS RATIO can be discarded without losing information.,6703
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or RADIUS RATIO can be discarded without losing information.,6704
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or RADIUS RATIO can be discarded without losing information.,6705
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or RADIUS RATIO can be discarded without losing information.,6706
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or RADIUS RATIO can be discarded without losing information.,6707
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or RADIUS RATIO can be discarded without losing information.,6708
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or RADIUS RATIO can be discarded without losing information.,6709
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or RADIUS RATIO can be discarded without losing information.,6710
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or RADIUS RATIO can be discarded without losing information.,6711
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or RADIUS RATIO can be discarded without losing information.,6712
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or RADIUS RATIO can be discarded without losing information.,6713
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or RADIUS RATIO can be discarded without losing information.,6714
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or RADIUS RATIO can be discarded without losing information.,6715
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or RADIUS RATIO can be discarded without losing information.,6716
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or RADIUS RATIO can be discarded without losing information.,6717
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or RADIUS RATIO can be discarded without losing information.,6718
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or RADIUS RATIO can be discarded without losing information.,6719
vehicle_correlation_heatmap.png,One of the variables target or RADIUS RATIO can be discarded without losing information.,6720
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or PR AXIS ASPECT RATIO can be discarded without losing information.,6721
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or PR AXIS ASPECT RATIO can be discarded without losing information.,6722
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or PR AXIS ASPECT RATIO can be discarded without losing information.,6723
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6724
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6725
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6726
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or PR AXIS ASPECT RATIO can be discarded without losing information.,6727
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or PR AXIS ASPECT RATIO can be discarded without losing information.,6728
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or PR AXIS ASPECT RATIO can be discarded without losing information.,6729
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or PR AXIS ASPECT RATIO can be discarded without losing information.,6730
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or PR AXIS ASPECT RATIO can be discarded without losing information.,6731
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or PR AXIS ASPECT RATIO can be discarded without losing information.,6732
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or PR AXIS ASPECT RATIO can be discarded without losing information.,6733
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or PR AXIS ASPECT RATIO can be discarded without losing information.,6734
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or PR AXIS ASPECT RATIO can be discarded without losing information.,6735
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or PR AXIS ASPECT RATIO can be discarded without losing information.,6736
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6737
vehicle_correlation_heatmap.png,One of the variables target or PR AXIS ASPECT RATIO can be discarded without losing information.,6738
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6739
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6740
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6741
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6742
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6743
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6744
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6745
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6746
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6747
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6748
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6749
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6750
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6751
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6752
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6753
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6754
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6755
vehicle_correlation_heatmap.png,One of the variables target or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6756
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or SCATTER RATIO can be discarded without losing information.,6757
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or SCATTER RATIO can be discarded without losing information.,6758
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or SCATTER RATIO can be discarded without losing information.,6759
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or SCATTER RATIO can be discarded without losing information.,6760
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or SCATTER RATIO can be discarded without losing information.,6761
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or SCATTER RATIO can be discarded without losing information.,6762
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or SCATTER RATIO can be discarded without losing information.,6763
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or SCATTER RATIO can be discarded without losing information.,6764
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or SCATTER RATIO can be discarded without losing information.,6765
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or SCATTER RATIO can be discarded without losing information.,6766
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or SCATTER RATIO can be discarded without losing information.,6767
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or SCATTER RATIO can be discarded without losing information.,6768
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or SCATTER RATIO can be discarded without losing information.,6769
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or SCATTER RATIO can be discarded without losing information.,6770
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or SCATTER RATIO can be discarded without losing information.,6771
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or SCATTER RATIO can be discarded without losing information.,6772
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or SCATTER RATIO can be discarded without losing information.,6773
vehicle_correlation_heatmap.png,One of the variables target or SCATTER RATIO can be discarded without losing information.,6774
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or ELONGATEDNESS can be discarded without losing information.,6775
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or ELONGATEDNESS can be discarded without losing information.,6776
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or ELONGATEDNESS can be discarded without losing information.,6777
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or ELONGATEDNESS can be discarded without losing information.,6778
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or ELONGATEDNESS can be discarded without losing information.,6779
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or ELONGATEDNESS can be discarded without losing information.,6780
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or ELONGATEDNESS can be discarded without losing information.,6781
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or ELONGATEDNESS can be discarded without losing information.,6782
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or ELONGATEDNESS can be discarded without losing information.,6783
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or ELONGATEDNESS can be discarded without losing information.,6784
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or ELONGATEDNESS can be discarded without losing information.,6785
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or ELONGATEDNESS can be discarded without losing information.,6786
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or ELONGATEDNESS can be discarded without losing information.,6787
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or ELONGATEDNESS can be discarded without losing information.,6788
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or ELONGATEDNESS can be discarded without losing information.,6789
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or ELONGATEDNESS can be discarded without losing information.,6790
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or ELONGATEDNESS can be discarded without losing information.,6791
vehicle_correlation_heatmap.png,One of the variables target or ELONGATEDNESS can be discarded without losing information.,6792
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or PR AXISRECTANGULAR can be discarded without losing information.,6793
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or PR AXISRECTANGULAR can be discarded without losing information.,6794
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or PR AXISRECTANGULAR can be discarded without losing information.,6795
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or PR AXISRECTANGULAR can be discarded without losing information.,6796
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or PR AXISRECTANGULAR can be discarded without losing information.,6797
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or PR AXISRECTANGULAR can be discarded without losing information.,6798
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or PR AXISRECTANGULAR can be discarded without losing information.,6799
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or PR AXISRECTANGULAR can be discarded without losing information.,6800
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or PR AXISRECTANGULAR can be discarded without losing information.,6801
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or PR AXISRECTANGULAR can be discarded without losing information.,6802
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or PR AXISRECTANGULAR can be discarded without losing information.,6803
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or PR AXISRECTANGULAR can be discarded without losing information.,6804
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or PR AXISRECTANGULAR can be discarded without losing information.,6805
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or PR AXISRECTANGULAR can be discarded without losing information.,6806
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or PR AXISRECTANGULAR can be discarded without losing information.,6807
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or PR AXISRECTANGULAR can be discarded without losing information.,6808
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or PR AXISRECTANGULAR can be discarded without losing information.,6809
vehicle_correlation_heatmap.png,One of the variables target or PR AXISRECTANGULAR can be discarded without losing information.,6810
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or LENGTHRECTANGULAR can be discarded without losing information.,6811
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or LENGTHRECTANGULAR can be discarded without losing information.,6812
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or LENGTHRECTANGULAR can be discarded without losing information.,6813
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or LENGTHRECTANGULAR can be discarded without losing information.,6814
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or LENGTHRECTANGULAR can be discarded without losing information.,6815
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or LENGTHRECTANGULAR can be discarded without losing information.,6816
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or LENGTHRECTANGULAR can be discarded without losing information.,6817
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or LENGTHRECTANGULAR can be discarded without losing information.,6818
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or LENGTHRECTANGULAR can be discarded without losing information.,6819
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or LENGTHRECTANGULAR can be discarded without losing information.,6820
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or LENGTHRECTANGULAR can be discarded without losing information.,6821
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or LENGTHRECTANGULAR can be discarded without losing information.,6822
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or LENGTHRECTANGULAR can be discarded without losing information.,6823
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or LENGTHRECTANGULAR can be discarded without losing information.,6824
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or LENGTHRECTANGULAR can be discarded without losing information.,6825
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or LENGTHRECTANGULAR can be discarded without losing information.,6826
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or LENGTHRECTANGULAR can be discarded without losing information.,6827
vehicle_correlation_heatmap.png,One of the variables target or LENGTHRECTANGULAR can be discarded without losing information.,6828
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MAJORVARIANCE can be discarded without losing information.,6829
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or MAJORVARIANCE can be discarded without losing information.,6830
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MAJORVARIANCE can be discarded without losing information.,6831
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MAJORVARIANCE can be discarded without losing information.,6832
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or MAJORVARIANCE can be discarded without losing information.,6833
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MAJORVARIANCE can be discarded without losing information.,6834
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MAJORVARIANCE can be discarded without losing information.,6835
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MAJORVARIANCE can be discarded without losing information.,6836
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MAJORVARIANCE can be discarded without losing information.,6837
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MAJORVARIANCE can be discarded without losing information.,6838
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or MAJORVARIANCE can be discarded without losing information.,6839
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MAJORVARIANCE can be discarded without losing information.,6840
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or MAJORVARIANCE can be discarded without losing information.,6841
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MAJORVARIANCE can be discarded without losing information.,6842
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MAJORVARIANCE can be discarded without losing information.,6843
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MAJORVARIANCE can be discarded without losing information.,6844
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MAJORVARIANCE can be discarded without losing information.,6845
vehicle_correlation_heatmap.png,One of the variables target or MAJORVARIANCE can be discarded without losing information.,6846
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MINORVARIANCE can be discarded without losing information.,6847
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or MINORVARIANCE can be discarded without losing information.,6848
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MINORVARIANCE can be discarded without losing information.,6849
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MINORVARIANCE can be discarded without losing information.,6850
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or MINORVARIANCE can be discarded without losing information.,6851
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MINORVARIANCE can be discarded without losing information.,6852
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MINORVARIANCE can be discarded without losing information.,6853
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MINORVARIANCE can be discarded without losing information.,6854
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MINORVARIANCE can be discarded without losing information.,6855
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MINORVARIANCE can be discarded without losing information.,6856
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MINORVARIANCE can be discarded without losing information.,6857
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MINORVARIANCE can be discarded without losing information.,6858
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or MINORVARIANCE can be discarded without losing information.,6859
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MINORVARIANCE can be discarded without losing information.,6860
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MINORVARIANCE can be discarded without losing information.,6861
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MINORVARIANCE can be discarded without losing information.,6862
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MINORVARIANCE can be discarded without losing information.,6863
vehicle_correlation_heatmap.png,One of the variables target or MINORVARIANCE can be discarded without losing information.,6864
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or GYRATIONRADIUS can be discarded without losing information.,6865
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or GYRATIONRADIUS can be discarded without losing information.,6866
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or GYRATIONRADIUS can be discarded without losing information.,6867
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or GYRATIONRADIUS can be discarded without losing information.,6868
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or GYRATIONRADIUS can be discarded without losing information.,6869
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or GYRATIONRADIUS can be discarded without losing information.,6870
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or GYRATIONRADIUS can be discarded without losing information.,6871
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or GYRATIONRADIUS can be discarded without losing information.,6872
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or GYRATIONRADIUS can be discarded without losing information.,6873
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or GYRATIONRADIUS can be discarded without losing information.,6874
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or GYRATIONRADIUS can be discarded without losing information.,6875
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or GYRATIONRADIUS can be discarded without losing information.,6876
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or GYRATIONRADIUS can be discarded without losing information.,6877
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or GYRATIONRADIUS can be discarded without losing information.,6878
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or GYRATIONRADIUS can be discarded without losing information.,6879
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or GYRATIONRADIUS can be discarded without losing information.,6880
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or GYRATIONRADIUS can be discarded without losing information.,6881
vehicle_correlation_heatmap.png,One of the variables target or GYRATIONRADIUS can be discarded without losing information.,6882
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MAJORSKEWNESS can be discarded without losing information.,6883
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or MAJORSKEWNESS can be discarded without losing information.,6884
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MAJORSKEWNESS can be discarded without losing information.,6885
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MAJORSKEWNESS can be discarded without losing information.,6886
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or MAJORSKEWNESS can be discarded without losing information.,6887
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MAJORSKEWNESS can be discarded without losing information.,6888
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MAJORSKEWNESS can be discarded without losing information.,6889
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MAJORSKEWNESS can be discarded without losing information.,6890
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MAJORSKEWNESS can be discarded without losing information.,6891
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MAJORSKEWNESS can be discarded without losing information.,6892
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MAJORSKEWNESS can be discarded without losing information.,6893
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or MAJORSKEWNESS can be discarded without losing information.,6894
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MAJORSKEWNESS can be discarded without losing information.,6895
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MAJORSKEWNESS can be discarded without losing information.,6896
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MAJORSKEWNESS can be discarded without losing information.,6897
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MAJORSKEWNESS can be discarded without losing information.,6898
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MAJORSKEWNESS can be discarded without losing information.,6899
vehicle_correlation_heatmap.png,One of the variables target or MAJORSKEWNESS can be discarded without losing information.,6900
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MINORSKEWNESS can be discarded without losing information.,6901
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or MINORSKEWNESS can be discarded without losing information.,6902
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MINORSKEWNESS can be discarded without losing information.,6903
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MINORSKEWNESS can be discarded without losing information.,6904
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or MINORSKEWNESS can be discarded without losing information.,6905
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MINORSKEWNESS can be discarded without losing information.,6906
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MINORSKEWNESS can be discarded without losing information.,6907
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MINORSKEWNESS can be discarded without losing information.,6908
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MINORSKEWNESS can be discarded without losing information.,6909
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MINORSKEWNESS can be discarded without losing information.,6910
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MINORSKEWNESS can be discarded without losing information.,6911
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or MINORSKEWNESS can be discarded without losing information.,6912
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MINORSKEWNESS can be discarded without losing information.,6913
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or MINORSKEWNESS can be discarded without losing information.,6914
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MINORSKEWNESS can be discarded without losing information.,6915
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MINORSKEWNESS can be discarded without losing information.,6916
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MINORSKEWNESS can be discarded without losing information.,6917
vehicle_correlation_heatmap.png,One of the variables target or MINORSKEWNESS can be discarded without losing information.,6918
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MINORKURTOSIS can be discarded without losing information.,6919
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or MINORKURTOSIS can be discarded without losing information.,6920
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MINORKURTOSIS can be discarded without losing information.,6921
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MINORKURTOSIS can be discarded without losing information.,6922
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or MINORKURTOSIS can be discarded without losing information.,6923
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MINORKURTOSIS can be discarded without losing information.,6924
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MINORKURTOSIS can be discarded without losing information.,6925
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MINORKURTOSIS can be discarded without losing information.,6926
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MINORKURTOSIS can be discarded without losing information.,6927
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MINORKURTOSIS can be discarded without losing information.,6928
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MINORKURTOSIS can be discarded without losing information.,6929
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or MINORKURTOSIS can be discarded without losing information.,6930
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MINORKURTOSIS can be discarded without losing information.,6931
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or MINORKURTOSIS can be discarded without losing information.,6932
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MINORKURTOSIS can be discarded without losing information.,6933
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MINORKURTOSIS can be discarded without losing information.,6934
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MINORKURTOSIS can be discarded without losing information.,6935
vehicle_correlation_heatmap.png,One of the variables target or MINORKURTOSIS can be discarded without losing information.,6936
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MAJORKURTOSIS can be discarded without losing information.,6937
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or MAJORKURTOSIS can be discarded without losing information.,6938
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MAJORKURTOSIS can be discarded without losing information.,6939
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MAJORKURTOSIS can be discarded without losing information.,6940
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or MAJORKURTOSIS can be discarded without losing information.,6941
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MAJORKURTOSIS can be discarded without losing information.,6942
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MAJORKURTOSIS can be discarded without losing information.,6943
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MAJORKURTOSIS can be discarded without losing information.,6944
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MAJORKURTOSIS can be discarded without losing information.,6945
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MAJORKURTOSIS can be discarded without losing information.,6946
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MAJORKURTOSIS can be discarded without losing information.,6947
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or MAJORKURTOSIS can be discarded without losing information.,6948
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MAJORKURTOSIS can be discarded without losing information.,6949
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or MAJORKURTOSIS can be discarded without losing information.,6950
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MAJORKURTOSIS can be discarded without losing information.,6951
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MAJORKURTOSIS can be discarded without losing information.,6952
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MAJORKURTOSIS can be discarded without losing information.,6953
vehicle_correlation_heatmap.png,One of the variables target or MAJORKURTOSIS can be discarded without losing information.,6954
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or HOLLOWS RATIO can be discarded without losing information.,6955
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or HOLLOWS RATIO can be discarded without losing information.,6956
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or HOLLOWS RATIO can be discarded without losing information.,6957
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or HOLLOWS RATIO can be discarded without losing information.,6958
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or HOLLOWS RATIO can be discarded without losing information.,6959
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or HOLLOWS RATIO can be discarded without losing information.,6960
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or HOLLOWS RATIO can be discarded without losing information.,6961
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or HOLLOWS RATIO can be discarded without losing information.,6962
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or HOLLOWS RATIO can be discarded without losing information.,6963
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or HOLLOWS RATIO can be discarded without losing information.,6964
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or HOLLOWS RATIO can be discarded without losing information.,6965
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or HOLLOWS RATIO can be discarded without losing information.,6966
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or HOLLOWS RATIO can be discarded without losing information.,6967
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or HOLLOWS RATIO can be discarded without losing information.,6968
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or HOLLOWS RATIO can be discarded without losing information.,6969
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or HOLLOWS RATIO can be discarded without losing information.,6970
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or HOLLOWS RATIO can be discarded without losing information.,6971
vehicle_correlation_heatmap.png,One of the variables target or HOLLOWS RATIO can be discarded without losing information.,6972
vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or target can be discarded without losing information.,6973
vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or target can be discarded without losing information.,6974
vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or target can be discarded without losing information.,6975
vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or target can be discarded without losing information.,6976
vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or target can be discarded without losing information.,6977
vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or target can be discarded without losing information.,6978
vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or target can be discarded without losing information.,6979
vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or target can be discarded without losing information.,6980
vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or target can be discarded without losing information.,6981
vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or target can be discarded without losing information.,6982
vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or target can be discarded without losing information.,6983
vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or target can be discarded without losing information.,6984
vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or target can be discarded without losing information.,6985
vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or target can be discarded without losing information.,6986
vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or target can be discarded without losing information.,6987
vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or target can be discarded without losing information.,6988
vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or target can be discarded without losing information.,6989
vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or target can be discarded without losing information.,6990
vehicle_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,6991
vehicle_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,6992
vehicle_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,6993
vehicle_boxplots.png,At least 50 of the variables present outliers.,6994
vehicle_histograms_numeric.png,At least 60 of the variables present outliers.,6995
vehicle_histograms_numeric.png,At least 75 of the variables present outliers.,6996
vehicle_histograms_numeric.png,At least 85 of the variables present outliers.,6997
vehicle_histograms_numeric.png,Variable COMPACTNESS presents some outliers.,6998
vehicle_boxplots.png,Variable CIRCULARITY presents some outliers.,6999
vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY presents some outliers.,7000
vehicle_boxplots.png,Variable RADIUS RATIO presents some outliers.,7001
vehicle_histograms_numeric.png,Variable PR AXIS ASPECT RATIO presents some outliers.,7002
vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO presents some outliers.,7003
vehicle_histograms_numeric.png,Variable SCATTER RATIO presents some outliers.,7004
vehicle_boxplots.png,Variable ELONGATEDNESS presents some outliers.,7005
vehicle_boxplots.png,Variable PR AXISRECTANGULAR presents some outliers.,7006
vehicle_boxplots.png,Variable LENGTHRECTANGULAR presents some outliers.,7007
vehicle_histograms_numeric.png,Variable MAJORVARIANCE presents some outliers.,7008
vehicle_histograms_numeric.png,Variable MINORVARIANCE presents some outliers.,7009
vehicle_histograms_numeric.png,Variable GYRATIONRADIUS presents some outliers.,7010
vehicle_boxplots.png,Variable MAJORSKEWNESS presents some outliers.,7011
vehicle_histograms_numeric.png,Variable MINORSKEWNESS presents some outliers.,7012
vehicle_histograms_numeric.png,Variable MINORKURTOSIS presents some outliers.,7013
vehicle_histograms_numeric.png,Variable MAJORKURTOSIS presents some outliers.,7014
vehicle_boxplots.png,Variable HOLLOWS RATIO presents some outliers.,7015
vehicle_boxplots.png,Variable target presents some outliers.,7016
vehicle_boxplots.png,Variable COMPACTNESS doesn’t have any outliers.,7017
vehicle_histograms_numeric.png,Variable CIRCULARITY doesn’t have any outliers.,7018
vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY doesn’t have any outliers.,7019
vehicle_boxplots.png,Variable RADIUS RATIO doesn’t have any outliers.,7020
vehicle_histograms_numeric.png,Variable PR AXIS ASPECT RATIO doesn’t have any outliers.,7021
vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO doesn’t have any outliers.,7022
vehicle_histograms_numeric.png,Variable SCATTER RATIO doesn’t have any outliers.,7023
vehicle_boxplots.png,Variable ELONGATEDNESS doesn’t have any outliers.,7024
vehicle_boxplots.png,Variable PR AXISRECTANGULAR doesn’t have any outliers.,7025
vehicle_boxplots.png,Variable LENGTHRECTANGULAR doesn’t have any outliers.,7026
vehicle_boxplots.png,Variable MAJORVARIANCE doesn’t have any outliers.,7027
vehicle_histograms_numeric.png,Variable MINORVARIANCE doesn’t have any outliers.,7028
vehicle_boxplots.png,Variable GYRATIONRADIUS doesn’t have any outliers.,7029
vehicle_boxplots.png,Variable MAJORSKEWNESS doesn’t have any outliers.,7030
vehicle_histograms_numeric.png,Variable MINORSKEWNESS doesn’t have any outliers.,7031
vehicle_histograms_numeric.png,Variable MINORKURTOSIS doesn’t have any outliers.,7032
vehicle_boxplots.png,Variable MAJORKURTOSIS doesn’t have any outliers.,7033
vehicle_boxplots.png,Variable HOLLOWS RATIO doesn’t have any outliers.,7034
vehicle_histograms_numeric.png,Variable target doesn’t have any outliers.,7035
vehicle_histograms_numeric.png,Variable COMPACTNESS shows some outlier values.,7036
vehicle_histograms_numeric.png,Variable CIRCULARITY shows some outlier values.,7037
vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY shows some outlier values.,7038
vehicle_histograms_numeric.png,Variable RADIUS RATIO shows some outlier values.,7039
vehicle_boxplots.png,Variable PR AXIS ASPECT RATIO shows some outlier values.,7040
vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO shows some outlier values.,7041
vehicle_histograms_numeric.png,Variable SCATTER RATIO shows some outlier values.,7042
vehicle_histograms_numeric.png,Variable ELONGATEDNESS shows some outlier values.,7043
vehicle_boxplots.png,Variable PR AXISRECTANGULAR shows some outlier values.,7044
vehicle_histograms_numeric.png,Variable LENGTHRECTANGULAR shows some outlier values.,7045
vehicle_histograms_numeric.png,Variable MAJORVARIANCE shows some outlier values.,7046
vehicle_histograms_numeric.png,Variable MINORVARIANCE shows some outlier values.,7047
vehicle_histograms_numeric.png,Variable GYRATIONRADIUS shows some outlier values.,7048
vehicle_histograms_numeric.png,Variable MAJORSKEWNESS shows some outlier values.,7049
vehicle_boxplots.png,Variable MINORSKEWNESS shows some outlier values.,7050
vehicle_histograms_numeric.png,Variable MINORKURTOSIS shows some outlier values.,7051
vehicle_boxplots.png,Variable MAJORKURTOSIS shows some outlier values.,7052
vehicle_histograms_numeric.png,Variable HOLLOWS RATIO shows some outlier values.,7053
vehicle_histograms_numeric.png,Variable target shows some outlier values.,7054
vehicle_boxplots.png,Variable COMPACTNESS shows a high number of outlier values.,7055
vehicle_boxplots.png,Variable CIRCULARITY shows a high number of outlier values.,7056
vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY shows a high number of outlier values.,7057
vehicle_boxplots.png,Variable RADIUS RATIO shows a high number of outlier values.,7058
vehicle_boxplots.png,Variable PR AXIS ASPECT RATIO shows a high number of outlier values.,7059
vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO shows a high number of outlier values.,7060
vehicle_histograms_numeric.png,Variable SCATTER RATIO shows a high number of outlier values.,7061
vehicle_histograms_numeric.png,Variable ELONGATEDNESS shows a high number of outlier values.,7062
vehicle_histograms_numeric.png,Variable PR AXISRECTANGULAR shows a high number of outlier values.,7063
vehicle_histograms_numeric.png,Variable LENGTHRECTANGULAR shows a high number of outlier values.,7064
vehicle_boxplots.png,Variable MAJORVARIANCE shows a high number of outlier values.,7065
vehicle_boxplots.png,Variable MINORVARIANCE shows a high number of outlier values.,7066
vehicle_histograms_numeric.png,Variable GYRATIONRADIUS shows a high number of outlier values.,7067
vehicle_histograms_numeric.png,Variable MAJORSKEWNESS shows a high number of outlier values.,7068
vehicle_boxplots.png,Variable MINORSKEWNESS shows a high number of outlier values.,7069
vehicle_boxplots.png,Variable MINORKURTOSIS shows a high number of outlier values.,7070
vehicle_histograms_numeric.png,Variable MAJORKURTOSIS shows a high number of outlier values.,7071
vehicle_histograms_numeric.png,Variable HOLLOWS RATIO shows a high number of outlier values.,7072
vehicle_boxplots.png,Variable target shows a high number of outlier values.,7073
vehicle_histograms_numeric.png,Outliers seem to be a problem in the dataset.,7074
vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7075
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7076
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7077
vehicle_histograms_numeric.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7078
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7079
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7080
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7081
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7082
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7083
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7084
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7085
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7086
vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7087
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7088
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7089
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7090
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7091
vehicle_boxplots.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7092
vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7093
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7094
vehicle_histograms_numeric.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7095
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7096
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7097
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7098
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7099
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7100
vehicle_boxplots.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7101
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7102
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7103
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7104
vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7105
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7106
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7107
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7108
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7109
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7110
vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7111
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7112
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7113
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7114
vehicle_histograms_numeric.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7115
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7116
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7117
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7118
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7119
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7120
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7121
vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7122
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7123
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7124
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7125
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7126
vehicle_boxplots.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7127
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7128
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7129
vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7130
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7131
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7132
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7133
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7134
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7135
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7136
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7137
vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7138
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7139
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7140
vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7141
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7142
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7143
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7144
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7145
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7146
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7147
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7148
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7149
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7150
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7151
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7152
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7153
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7154
vehicle_boxplots.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7155
vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7156
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7157
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7158
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7159
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7160
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7161
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7162
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7163
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7164
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7165
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7166
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7167
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7168
vehicle_histograms_numeric.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7169
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7170
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7171
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7172
vehicle_boxplots.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7173
vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7174
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7175
vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7176
vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7177
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7178
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7179
vehicle_boxplots.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7180
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7181
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7182
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7183
vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7184
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7185
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7186
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7187
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7188
vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7189
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7190
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7191
vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7192
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7193
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7194
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7195
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7196
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7197
vehicle_boxplots.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7198
vehicle_boxplots.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7199
vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7200
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7201
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7202
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7203
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7204
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7205
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7206
vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7207
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7208
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7209
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7210
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7211
vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7212
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7213
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7214
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7215
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7216
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7217
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7218
vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7219
vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7220
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7221
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7222
vehicle_boxplots.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7223
vehicle_histograms_numeric.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7224
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7225
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7226
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7227
vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7228
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7229
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7230
vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7231
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7232
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7233
vehicle_boxplots.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7234
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7235
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7236
vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7237
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7238
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7239
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7240
vehicle_boxplots.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7241
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7242
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7243
vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7244
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7245
vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7246
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7247
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7248
vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7249
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7250
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7251
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7252
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7253
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7254
vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7255
vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7256
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7257
vehicle_histograms_numeric.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7258
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7259
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7260
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7261
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7262
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7263
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7264
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7265
vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7266
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7267
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7268
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7269
vehicle_boxplots.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7270
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7271
vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7272
vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7273
vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7274
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7275
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7276
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7277
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7278
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7279
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7280
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7281
vehicle_boxplots.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7282
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7283
vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7284
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7285
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7286
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7287
vehicle_boxplots.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7288
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7289
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7290
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7291
vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7292
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7293
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7294
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7295
vehicle_histograms_numeric.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7296
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7297
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7298
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7299
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7300
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7301
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7302
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7303
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7304
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7305
vehicle_boxplots.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7306
vehicle_boxplots.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7307
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7308
vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7309
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7310
vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7311
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7312
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7313
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7314
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7315
vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7316
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7317
vehicle_boxplots.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7318
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7319
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7320
vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7321
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7322
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7323
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7324
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7325
vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7326
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7327
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7328
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7329
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7330
vehicle_boxplots.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7331
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7332
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7333
vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7334
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7335
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7336
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7337
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7338
vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7339
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7340
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7341
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7342
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7343
vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7344
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7345
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7346
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7347
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7348
vehicle_boxplots.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7349
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7350
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7351
vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7352
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7353
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7354
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7355
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7356
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7357
vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7358
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7359
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7360
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7361
vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7362
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7363
vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7364
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7365
vehicle_histograms_numeric.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7366
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7367
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7368
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7369
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7370
vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7371
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7372
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7373
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7374
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7375
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7376
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7377
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7378
vehicle_boxplots.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7379
vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7380
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7381
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7382
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7383
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7384
vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7385
vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7386
vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7387
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7388
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7389
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7390
vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7391
vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7392
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7393
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7394
vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7395
vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7396
vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7397
vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable target.",7398
vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable target.",7399
vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable target.",7400
vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable target.",7401
vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable target.",7402
vehicle_boxplots.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable target.",7403
vehicle_histograms_numeric.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable target.",7404
vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable target.",7405
vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable target.",7406
vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable target.",7407
vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable target.",7408
vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable target.",7409
vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable target.",7410
vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable target.",7411
vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable target.",7412
vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable target.",7413
vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable target.",7414
vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable target.",7415
vehicle_boxplots.png,Those boxplots show that the data is not normalized.,7416
vehicle_histograms_numeric.png,Variable COMPACTNESS is balanced.,7417
vehicle_boxplots.png,Variable CIRCULARITY is balanced.,7418
vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY is balanced.,7419
vehicle_boxplots.png,Variable RADIUS RATIO is balanced.,7420
vehicle_boxplots.png,Variable PR AXIS ASPECT RATIO is balanced.,7421
vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO is balanced.,7422
vehicle_boxplots.png,Variable SCATTER RATIO is balanced.,7423
vehicle_boxplots.png,Variable ELONGATEDNESS is balanced.,7424
vehicle_boxplots.png,Variable PR AXISRECTANGULAR is balanced.,7425
vehicle_histograms_numeric.png,Variable LENGTHRECTANGULAR is balanced.,7426
vehicle_boxplots.png,Variable MAJORVARIANCE is balanced.,7427
vehicle_histograms_numeric.png,Variable MINORVARIANCE is balanced.,7428
vehicle_boxplots.png,Variable GYRATIONRADIUS is balanced.,7429
vehicle_histograms_numeric.png,Variable MAJORSKEWNESS is balanced.,7430
vehicle_histograms_numeric.png,Variable MINORSKEWNESS is balanced.,7431
vehicle_boxplots.png,Variable MINORKURTOSIS is balanced.,7432
vehicle_boxplots.png,Variable MAJORKURTOSIS is balanced.,7433
vehicle_boxplots.png,Variable HOLLOWS RATIO is balanced.,7434
vehicle_boxplots.png,Variable target is balanced.,7435
vehicle_histograms.png,The variable COMPACTNESS can be seen as ordinal without losing information.,7436
vehicle_histograms.png,The variable CIRCULARITY can be seen as ordinal without losing information.,7437
vehicle_histograms.png,The variable DISTANCE CIRCULARITY can be seen as ordinal without losing information.,7438
vehicle_histograms.png,The variable RADIUS RATIO can be seen as ordinal without losing information.,7439
vehicle_histograms.png,The variable PR AXIS ASPECT RATIO can be seen as ordinal without losing information.,7440
vehicle_histograms.png,The variable MAX LENGTH ASPECT RATIO can be seen as ordinal without losing information.,7441
vehicle_histograms.png,The variable SCATTER RATIO can be seen as ordinal without losing information.,7442
vehicle_histograms.png,The variable ELONGATEDNESS can be seen as ordinal without losing information.,7443
vehicle_histograms.png,The variable PR AXISRECTANGULAR can be seen as ordinal without losing information.,7444
vehicle_histograms.png,The variable LENGTHRECTANGULAR can be seen as ordinal without losing information.,7445
vehicle_histograms.png,The variable MAJORVARIANCE can be seen as ordinal without losing information.,7446
vehicle_histograms.png,The variable MINORVARIANCE can be seen as ordinal without losing information.,7447
vehicle_histograms.png,The variable GYRATIONRADIUS can be seen as ordinal without losing information.,7448
vehicle_histograms.png,The variable MAJORSKEWNESS can be seen as ordinal without losing information.,7449
vehicle_histograms.png,The variable MINORSKEWNESS can be seen as ordinal without losing information.,7450
vehicle_histograms.png,The variable MINORKURTOSIS can be seen as ordinal without losing information.,7451
vehicle_histograms.png,The variable MAJORKURTOSIS can be seen as ordinal without losing information.,7452
vehicle_histograms.png,The variable HOLLOWS RATIO can be seen as ordinal without losing information.,7453
vehicle_histograms.png,The variable COMPACTNESS can be seen as ordinal.,7454
vehicle_histograms.png,The variable CIRCULARITY can be seen as ordinal.,7455
vehicle_histograms.png,The variable DISTANCE CIRCULARITY can be seen as ordinal.,7456
vehicle_histograms.png,The variable RADIUS RATIO can be seen as ordinal.,7457
vehicle_histograms.png,The variable PR AXIS ASPECT RATIO can be seen as ordinal.,7458
vehicle_histograms.png,The variable MAX LENGTH ASPECT RATIO can be seen as ordinal.,7459
vehicle_histograms.png,The variable SCATTER RATIO can be seen as ordinal.,7460
vehicle_histograms.png,The variable ELONGATEDNESS can be seen as ordinal.,7461
vehicle_histograms.png,The variable PR AXISRECTANGULAR can be seen as ordinal.,7462
vehicle_histograms.png,The variable LENGTHRECTANGULAR can be seen as ordinal.,7463
vehicle_histograms.png,The variable MAJORVARIANCE can be seen as ordinal.,7464
vehicle_histograms.png,The variable MINORVARIANCE can be seen as ordinal.,7465
vehicle_histograms.png,The variable GYRATIONRADIUS can be seen as ordinal.,7466
vehicle_histograms.png,The variable MAJORSKEWNESS can be seen as ordinal.,7467
vehicle_histograms.png,The variable MINORSKEWNESS can be seen as ordinal.,7468
vehicle_histograms.png,The variable MINORKURTOSIS can be seen as ordinal.,7469
vehicle_histograms.png,The variable MAJORKURTOSIS can be seen as ordinal.,7470
vehicle_histograms.png,The variable HOLLOWS RATIO can be seen as ordinal.,7471
vehicle_histograms.png,"All variables, but the class, should be dealt with as numeric.",7472
vehicle_histograms.png,"All variables, but the class, should be dealt with as binary.",7473
vehicle_histograms.png,"All variables, but the class, should be dealt with as date.",7474
vehicle_histograms.png,"All variables, but the class, should be dealt with as symbolic.",7475
vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 25.,7476
vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 45.,7477
vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 92.,7478
vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 59.,7479
vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 84.,7480
vehicle_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,7481
vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",7482
vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",7483
vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",7484
vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",7485
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A,B), as Iris-setosa.",7486
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, B), as Iris-setosa.",7487
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A, not B), as Iris-setosa.",7488
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as Iris-setosa.",7489
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A,B), as Iris-versicolor.",7490
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, B), as Iris-versicolor.",7491
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A, not B), as Iris-versicolor.",7492
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as Iris-versicolor.",7493
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A,B), as Iris-virginica.",7494
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, B), as Iris-virginica.",7495
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A, not B), as Iris-virginica.",7496
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as Iris-virginica.",7497
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-setosa for any k ≤ 35.",7498
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-setosa for any k ≤ 35.",7499
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-setosa for any k ≤ 35.",7500
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-setosa for any k ≤ 35.",7501
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-versicolor for any k ≤ 35.",7502
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-versicolor for any k ≤ 35.",7503
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-versicolor for any k ≤ 35.",7504
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-versicolor for any k ≤ 35.",7505
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-virginica for any k ≤ 35.",7506
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-virginica for any k ≤ 35.",7507
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-virginica for any k ≤ 35.",7508
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-virginica for any k ≤ 35.",7509
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-setosa for any k ≤ 38.",7510
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-setosa for any k ≤ 38.",7511
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-setosa for any k ≤ 38.",7512
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-setosa for any k ≤ 38.",7513
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-versicolor for any k ≤ 38.",7514
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-versicolor for any k ≤ 38.",7515
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-versicolor for any k ≤ 38.",7516
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-versicolor for any k ≤ 38.",7517
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-virginica for any k ≤ 38.",7518
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-virginica for any k ≤ 38.",7519
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-virginica for any k ≤ 38.",7520
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-virginica for any k ≤ 38.",7521
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-setosa for any k ≤ 32.",7522
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-setosa for any k ≤ 32.",7523
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-setosa for any k ≤ 32.",7524
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-setosa for any k ≤ 32.",7525
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-versicolor for any k ≤ 32.",7526
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-versicolor for any k ≤ 32.",7527
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-versicolor for any k ≤ 32.",7528
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-versicolor for any k ≤ 32.",7529
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-virginica for any k ≤ 32.",7530
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-virginica for any k ≤ 32.",7531
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-virginica for any k ≤ 32.",7532
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-virginica for any k ≤ 32.",7533
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A,B) as Iris-setosa.",7534
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, B) as Iris-setosa.",7535
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A, not B) as Iris-setosa.",7536
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, not B) as Iris-setosa.",7537
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A,B) as Iris-versicolor.",7538
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, B) as Iris-versicolor.",7539
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A, not B) as Iris-versicolor.",7540
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, not B) as Iris-versicolor.",7541
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A,B) as Iris-virginica.",7542
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, B) as Iris-virginica.",7543
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A, not B) as Iris-virginica.",7544
Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, not B) as Iris-virginica.",7545
Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 598 episodes.,7546
Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 566 episodes.,7547
Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 939 episodes.,7548
Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 541 episodes.,7549
Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 767 episodes.,7550
Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 148 estimators.,7551
Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 72 estimators.,7552
Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 67 estimators.,7553
Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 98 estimators.,7554
Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 55 estimators.,7555
Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 87 estimators.,7556
Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 90 estimators.,7557
Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 97 estimators.,7558
Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 109 estimators.,7559
Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 54 estimators.,7560
Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,7561
Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,7562
Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,7563
Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,7564
Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,7565
Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,7566
Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,7567
Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,7568
Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,7569
Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,7570
Iris_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,7571
Iris_decision_tree.png,The recall for the presented tree is higher than its accuracy.,7572
Iris_decision_tree.png,The precision for the presented tree is higher than its accuracy.,7573
Iris_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,7574
Iris_decision_tree.png,The recall for the presented tree is lower than its accuracy.,7575
Iris_decision_tree.png,The precision for the presented tree is lower than its accuracy.,7576
Iris_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,7577
Iris_decision_tree.png,The accuracy for the presented tree is higher than its recall.,7578
Iris_decision_tree.png,The precision for the presented tree is higher than its recall.,7579
Iris_decision_tree.png,The specificity for the presented tree is higher than its recall.,7580
Iris_decision_tree.png,The accuracy for the presented tree is lower than its recall.,7581
Iris_decision_tree.png,The precision for the presented tree is lower than its recall.,7582
Iris_decision_tree.png,The specificity for the presented tree is lower than its recall.,7583
Iris_decision_tree.png,The accuracy for the presented tree is higher than its precision.,7584
Iris_decision_tree.png,The recall for the presented tree is higher than its precision.,7585
Iris_decision_tree.png,The specificity for the presented tree is higher than its precision.,7586
Iris_decision_tree.png,The accuracy for the presented tree is lower than its precision.,7587
Iris_decision_tree.png,The recall for the presented tree is lower than its precision.,7588
Iris_decision_tree.png,The specificity for the presented tree is lower than its precision.,7589
Iris_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,7590
Iris_decision_tree.png,The recall for the presented tree is higher than its specificity.,7591
Iris_decision_tree.png,The precision for the presented tree is higher than its specificity.,7592
Iris_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,7593
Iris_decision_tree.png,The recall for the presented tree is lower than its specificity.,7594
Iris_decision_tree.png,The precision for the presented tree is lower than its specificity.,7595
Iris_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,7596
Iris_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,7597
Iris_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,7598
Iris_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,7599
Iris_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,7600
Iris_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,7601
Iris_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,7602
Iris_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,7603
Iris_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,7604
Iris_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,7605
Iris_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,7606
Iris_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,7607
Iris_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,7608
Iris_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,7609
Iris_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,7610
Iris_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,7611
Iris_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,7612
Iris_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,7613
Iris_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,7614
Iris_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,7615
Iris_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,7616
Iris_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,7617
Iris_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,7618
Iris_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,7619
Iris_decision_tree.png,The number of True Positives reported in the same tree is 27.,7620
Iris_decision_tree.png,The number of False Positives reported in the same tree is 17.,7621
Iris_decision_tree.png,The number of True Negatives reported in the same tree is 41.,7622
Iris_decision_tree.png,The number of False Negatives reported in the same tree is 29.,7623
Iris_decision_tree.png,The number of True Positives reported in the same tree is 21.,7624
Iris_decision_tree.png,The number of False Positives reported in the same tree is 14.,7625
Iris_decision_tree.png,The number of True Negatives reported in the same tree is 45.,7626
Iris_decision_tree.png,The number of False Negatives reported in the same tree is 49.,7627
Iris_decision_tree.png,The number of True Positives reported in the same tree is 43.,7628
Iris_decision_tree.png,The number of False Positives reported in the same tree is 10.,7629
Iris_decision_tree.png,The number of True Negatives reported in the same tree is 37.,7630
Iris_decision_tree.png,The number of False Negatives reported in the same tree is 33.,7631
Iris_decision_tree.png,The number of True Positives reported in the same tree is 47.,7632
Iris_decision_tree.png,The number of False Positives reported in the same tree is 15.,7633
Iris_decision_tree.png,The number of True Negatives reported in the same tree is 46.,7634
Iris_decision_tree.png,The number of False Negatives reported in the same tree is 24.,7635
Iris_decision_tree.png,The number of True Positives reported in the same tree is 32.,7636
Iris_decision_tree.png,The number of False Positives reported in the same tree is 26.,7637
Iris_decision_tree.png,The number of True Negatives reported in the same tree is 42.,7638
Iris_decision_tree.png,The number of False Negatives reported in the same tree is 18.,7639
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,7640
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,7641
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,7642
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,7643
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,7644
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,7645
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,7646
Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,7647
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,7648
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,7649
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,7650
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,7651
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,7652
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,7653
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,7654
Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,7655
Iris_decision_tree.png,The accuracy for the presented tree is higher than 65%.,7656
Iris_decision_tree.png,The recall for the presented tree is higher than 62%.,7657
Iris_decision_tree.png,The precision for the presented tree is higher than 76%.,7658
Iris_decision_tree.png,The specificity for the presented tree is higher than 68%.,7659
Iris_decision_tree.png,The accuracy for the presented tree is lower than 73%.,7660
Iris_decision_tree.png,The recall for the presented tree is lower than 79%.,7661
Iris_decision_tree.png,The precision for the presented tree is lower than 80%.,7662
Iris_decision_tree.png,The specificity for the presented tree is lower than 64%.,7663
Iris_decision_tree.png,The accuracy for the presented tree is higher than 63%.,7664
Iris_decision_tree.png,The recall for the presented tree is higher than 74%.,7665
Iris_decision_tree.png,The precision for the presented tree is higher than 87%.,7666
Iris_decision_tree.png,The specificity for the presented tree is higher than 86%.,7667
Iris_decision_tree.png,The accuracy for the presented tree is lower than 78%.,7668
Iris_decision_tree.png,The recall for the presented tree is lower than 67%.,7669
Iris_decision_tree.png,The precision for the presented tree is lower than 83%.,7670
Iris_decision_tree.png,The specificity for the presented tree is lower than 81%.,7671
Iris_decision_tree.png,The accuracy for the presented tree is higher than 70%.,7672
Iris_decision_tree.png,The recall for the presented tree is higher than 85%.,7673
Iris_decision_tree.png,The precision for the presented tree is higher than 82%.,7674
Iris_decision_tree.png,The specificity for the presented tree is higher than 89%.,7675
Iris_decision_tree.png,The accuracy for the presented tree is lower than 84%.,7676
Iris_decision_tree.png,The recall for the presented tree is lower than 66%.,7677
Iris_decision_tree.png,The precision for the presented tree is lower than 69%.,7678
Iris_decision_tree.png,The specificity for the presented tree is lower than 72%.,7679
Iris_decision_tree.png,The accuracy for the presented tree is higher than 90%.,7680
Iris_decision_tree.png,The recall for the presented tree is higher than 61%.,7681
Iris_decision_tree.png,The precision for the presented tree is higher than 88%.,7682
Iris_decision_tree.png,The specificity for the presented tree is higher than 75%.,7683
Iris_decision_tree.png,The accuracy for the presented tree is lower than 77%.,7684
Iris_decision_tree.png,The recall for the presented tree is lower than 60%.,7685
Iris_decision_tree.png,The precision for the presented tree is lower than 71%.,7686
Iris_decision_tree.png,The specificity for the presented tree is lower than 87%.,7687
Iris_decision_tree.png,The accuracy for the presented tree is higher than 70%.,7688
Iris_decision_tree.png,The recall for the presented tree is higher than 65%.,7689
Iris_decision_tree.png,The precision for the presented tree is higher than 80%.,7690
Iris_decision_tree.png,The specificity for the presented tree is higher than 65%.,7691
Iris_decision_tree.png,The accuracy for the presented tree is lower than 85%.,7692
Iris_decision_tree.png,The recall for the presented tree is lower than 87%.,7693
Iris_decision_tree.png,The precision for the presented tree is lower than 88%.,7694
Iris_decision_tree.png,The specificity for the presented tree is lower than 62%.,7695
Iris_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",7696
Iris_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",7697
Iris_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",7698
Iris_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",7699
Iris_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",7700
Iris_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",7701
Iris_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,7702
Iris_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,7703
Iris_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,7704
Iris_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,7705
Iris_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,7706
Iris_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,7707
Iris_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,7708
Iris_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,7709
Iris_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,7710
Iris_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,7711
Iris_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,7712
Iris_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,7713
Iris_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,7714
Iris_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,7715
Iris_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,7716
Iris_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,7717
Iris_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,7718
Iris_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,7719
Iris_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,7720
Iris_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,7721
Iris_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,7722
Iris_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,7723
Iris_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,7724
Iris_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,7725
Iris_overfitting_knn.png,KNN is in overfitting for k less than 2.,7726
Iris_overfitting_knn.png,KNN is in overfitting for k larger than 2.,7727
Iris_overfitting_knn.png,KNN is in overfitting for k less than 3.,7728
Iris_overfitting_knn.png,KNN is in overfitting for k larger than 3.,7729
Iris_overfitting_knn.png,KNN is in overfitting for k less than 4.,7730
Iris_overfitting_knn.png,KNN is in overfitting for k larger than 4.,7731
Iris_overfitting_knn.png,KNN is in overfitting for k less than 5.,7732
Iris_overfitting_knn.png,KNN is in overfitting for k larger than 5.,7733
Iris_overfitting_knn.png,KNN is in overfitting for k less than 6.,7734
Iris_overfitting_knn.png,KNN is in overfitting for k larger than 6.,7735
Iris_overfitting_knn.png,KNN is in overfitting for k less than 7.,7736
Iris_overfitting_knn.png,KNN is in overfitting for k larger than 7.,7737
Iris_overfitting_knn.png,KNN is in overfitting for k less than 8.,7738
Iris_overfitting_knn.png,KNN is in overfitting for k larger than 8.,7739
Iris_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",7740
Iris_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",7741
Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",7742
Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",7743
Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",7744
Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",7745
Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",7746
Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",7747
Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",7748
Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",7749
Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",7750
Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",7751
Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",7752
Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",7753
Iris_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,7754
Iris_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,7755
Iris_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,7756
Iris_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,7757
Iris_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,7758
Iris_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,7759
Iris_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,7760
Iris_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,7761
Iris_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,7762
Iris_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,7763
Iris_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,7764
Iris_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,7765
Iris_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,7766
Iris_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,7767
Iris_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,7768
Iris_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,7769
Iris_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,7770
Iris_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,7771
Iris_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,7772
Iris_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,7773
Iris_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,7774
Iris_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,7775
Iris_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,7776
Iris_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,7777
Iris_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,7778
Iris_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,7779
Iris_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,7780
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalWidthCm previously than variable SepalLengthCm.,7781
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PetalLengthCm previously than variable SepalLengthCm.,7782
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PetalWidthCm previously than variable SepalLengthCm.,7783
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalLengthCm previously than variable SepalWidthCm.,7784
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PetalLengthCm previously than variable SepalWidthCm.,7785
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PetalWidthCm previously than variable SepalWidthCm.,7786
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalLengthCm previously than variable PetalLengthCm.,7787
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalWidthCm previously than variable PetalLengthCm.,7788
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PetalWidthCm previously than variable PetalLengthCm.,7789
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalLengthCm previously than variable PetalWidthCm.,7790
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalWidthCm previously than variable PetalWidthCm.,7791
Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PetalLengthCm previously than variable PetalWidthCm.,7792
Iris_pca.png,The first 2 principal components are enough for explaining half the data variance.,7793
Iris_pca.png,The first 3 principal components are enough for explaining half the data variance.,7794
Iris_pca.png,The first 4 principal components are enough for explaining half the data variance.,7795
Iris_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,7796
Iris_correlation_heatmap.png,Removing variable SepalLengthCm might improve the training of decision trees .,7797
Iris_correlation_heatmap.png,Removing variable SepalWidthCm might improve the training of decision trees .,7798
Iris_correlation_heatmap.png,Removing variable PetalLengthCm might improve the training of decision trees .,7799
Iris_correlation_heatmap.png,Removing variable PetalWidthCm might improve the training of decision trees .,7800
Iris_histograms.png,"Not knowing the semantics of SepalLengthCm variable, dummification could have been a more adequate codification.",7801
Iris_histograms.png,"Not knowing the semantics of SepalWidthCm variable, dummification could have been a more adequate codification.",7802
Iris_histograms.png,"Not knowing the semantics of PetalLengthCm variable, dummification could have been a more adequate codification.",7803
Iris_histograms.png,"Not knowing the semantics of PetalWidthCm variable, dummification could have been a more adequate codification.",7804
Iris_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,7805
Iris_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",7806
Iris_histograms.png,"Given the usual semantics of SepalLengthCm variable, dummification would have been a better codification.",7807
Iris_histograms.png,"Given the usual semantics of SepalWidthCm variable, dummification would have been a better codification.",7808
Iris_histograms.png,"Given the usual semantics of PetalLengthCm variable, dummification would have been a better codification.",7809
Iris_histograms.png,"Given the usual semantics of PetalWidthCm variable, dummification would have been a better codification.",7810
Iris_histograms.png,The variable SepalLengthCm can be coded as ordinal without losing information.,7811
Iris_histograms.png,The variable SepalWidthCm can be coded as ordinal without losing information.,7812
Iris_histograms.png,The variable PetalLengthCm can be coded as ordinal without losing information.,7813
Iris_histograms.png,The variable PetalWidthCm can be coded as ordinal without losing information.,7814
Iris_histograms.png,"Considering the common semantics for SepalLengthCm variable, dummification would be the most adequate encoding.",7815
Iris_histograms.png,"Considering the common semantics for SepalWidthCm variable, dummification would be the most adequate encoding.",7816
Iris_histograms.png,"Considering the common semantics for PetalLengthCm variable, dummification would be the most adequate encoding.",7817
Iris_histograms.png,"Considering the common semantics for PetalWidthCm variable, dummification would be the most adequate encoding.",7818
Iris_histograms.png,"Considering the common semantics for SepalWidthCm and SepalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7819
Iris_histograms.png,"Considering the common semantics for PetalLengthCm and SepalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7820
Iris_histograms.png,"Considering the common semantics for PetalWidthCm and SepalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7821
Iris_histograms.png,"Considering the common semantics for SepalLengthCm and SepalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7822
Iris_histograms.png,"Considering the common semantics for PetalLengthCm and SepalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7823
Iris_histograms.png,"Considering the common semantics for PetalWidthCm and SepalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7824
Iris_histograms.png,"Considering the common semantics for SepalLengthCm and PetalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7825
Iris_histograms.png,"Considering the common semantics for SepalWidthCm and PetalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7826
Iris_histograms.png,"Considering the common semantics for PetalWidthCm and PetalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7827
Iris_histograms.png,"Considering the common semantics for SepalLengthCm and PetalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7828
Iris_histograms.png,"Considering the common semantics for SepalWidthCm and PetalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7829
Iris_histograms.png,"Considering the common semantics for PetalLengthCm and PetalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7830
Iris_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,7831
Iris_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,7832
Iris_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,7833
Iris_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",7834
Iris_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",7835
Iris_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",7836
Iris_correlation_heatmap.png,Variables SepalWidthCm and SepalLengthCm seem to be useful for classification tasks.,7837
Iris_correlation_heatmap.png,Variables PetalLengthCm and SepalLengthCm seem to be useful for classification tasks.,7838
Iris_correlation_heatmap.png,Variables PetalWidthCm and SepalLengthCm seem to be useful for classification tasks.,7839
Iris_correlation_heatmap.png,Variables SepalLengthCm and SepalWidthCm seem to be useful for classification tasks.,7840
Iris_correlation_heatmap.png,Variables PetalLengthCm and SepalWidthCm seem to be useful for classification tasks.,7841
Iris_correlation_heatmap.png,Variables PetalWidthCm and SepalWidthCm seem to be useful for classification tasks.,7842
Iris_correlation_heatmap.png,Variables SepalLengthCm and PetalLengthCm seem to be useful for classification tasks.,7843
Iris_correlation_heatmap.png,Variables SepalWidthCm and PetalLengthCm seem to be useful for classification tasks.,7844
Iris_correlation_heatmap.png,Variables PetalWidthCm and PetalLengthCm seem to be useful for classification tasks.,7845
Iris_correlation_heatmap.png,Variables SepalLengthCm and PetalWidthCm seem to be useful for classification tasks.,7846
Iris_correlation_heatmap.png,Variables SepalWidthCm and PetalWidthCm seem to be useful for classification tasks.,7847
Iris_correlation_heatmap.png,Variables PetalLengthCm and PetalWidthCm seem to be useful for classification tasks.,7848
Iris_correlation_heatmap.png,Variable SepalLengthCm seems to be relevant for the majority of mining tasks.,7849
Iris_correlation_heatmap.png,Variable SepalWidthCm seems to be relevant for the majority of mining tasks.,7850
Iris_correlation_heatmap.png,Variable PetalLengthCm seems to be relevant for the majority of mining tasks.,7851
Iris_correlation_heatmap.png,Variable PetalWidthCm seems to be relevant for the majority of mining tasks.,7852
Iris_decision_tree.png,Variable SepalLengthCm is one of the most relevant variables.,7853
Iris_decision_tree.png,Variable SepalWidthCm is one of the most relevant variables.,7854
Iris_decision_tree.png,Variable PetalLengthCm is one of the most relevant variables.,7855
Iris_decision_tree.png,Variable PetalWidthCm is one of the most relevant variables.,7856
Iris_decision_tree.png,It is possible to state that SepalLengthCm is the first most discriminative variable regarding the class.,7857
Iris_decision_tree.png,It is possible to state that SepalWidthCm is the first most discriminative variable regarding the class.,7858
Iris_decision_tree.png,It is possible to state that PetalLengthCm is the first most discriminative variable regarding the class.,7859
Iris_decision_tree.png,It is possible to state that PetalWidthCm is the first most discriminative variable regarding the class.,7860
Iris_decision_tree.png,It is possible to state that SepalLengthCm is the second most discriminative variable regarding the class.,7861
Iris_decision_tree.png,It is possible to state that SepalWidthCm is the second most discriminative variable regarding the class.,7862
Iris_decision_tree.png,It is possible to state that PetalLengthCm is the second most discriminative variable regarding the class.,7863
Iris_decision_tree.png,It is possible to state that PetalWidthCm is the second most discriminative variable regarding the class.,7864
Iris_decision_tree.png,"The variable SepalLengthCm discriminates between the target values, as shown in the decision tree.",7865
Iris_decision_tree.png,"The variable SepalWidthCm discriminates between the target values, as shown in the decision tree.",7866
Iris_decision_tree.png,"The variable PetalLengthCm discriminates between the target values, as shown in the decision tree.",7867
Iris_decision_tree.png,"The variable PetalWidthCm discriminates between the target values, as shown in the decision tree.",7868
Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the two most relevant features.,7869
Iris_decision_tree.png,The variable SepalWidthCm seems to be one of the two most relevant features.,7870
Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the two most relevant features.,7871
Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the two most relevant features.,7872
Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the three most relevant features.,7873
Iris_decision_tree.png,The variable SepalWidthCm seems to be one of the three most relevant features.,7874
Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the three most relevant features.,7875
Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the three most relevant features.,7876
Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the four most relevant features.,7877
Iris_decision_tree.png,The variable SepalWidthCm seems to be one of the four most relevant features.,7878
Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the four most relevant features.,7879
Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the four most relevant features.,7880
Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the five most relevant features.,7881
Iris_decision_tree.png,The variable SepalWidthCm seems to be one of the five most relevant features.,7882
Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the five most relevant features.,7883
Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the five most relevant features.,7884
Iris_decision_tree.png,It is clear that variable SepalLengthCm is one of the two most relevant features.,7885
Iris_decision_tree.png,It is clear that variable SepalWidthCm is one of the two most relevant features.,7886
Iris_decision_tree.png,It is clear that variable PetalLengthCm is one of the two most relevant features.,7887
Iris_decision_tree.png,It is clear that variable PetalWidthCm is one of the two most relevant features.,7888
Iris_decision_tree.png,It is clear that variable SepalLengthCm is one of the three most relevant features.,7889
Iris_decision_tree.png,It is clear that variable SepalWidthCm is one of the three most relevant features.,7890
Iris_decision_tree.png,It is clear that variable PetalLengthCm is one of the three most relevant features.,7891
Iris_decision_tree.png,It is clear that variable PetalWidthCm is one of the three most relevant features.,7892
Iris_decision_tree.png,It is clear that variable SepalLengthCm is one of the four most relevant features.,7893
Iris_decision_tree.png,It is clear that variable SepalWidthCm is one of the four most relevant features.,7894
Iris_decision_tree.png,It is clear that variable PetalLengthCm is one of the four most relevant features.,7895
Iris_decision_tree.png,It is clear that variable PetalWidthCm is one of the four most relevant features.,7896
Iris_decision_tree.png,It is clear that variable SepalLengthCm is one of the five most relevant features.,7897
Iris_decision_tree.png,It is clear that variable SepalWidthCm is one of the five most relevant features.,7898
Iris_decision_tree.png,It is clear that variable PetalLengthCm is one of the five most relevant features.,7899
Iris_decision_tree.png,It is clear that variable PetalWidthCm is one of the five most relevant features.,7900
Iris_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",7901
Iris_correlation_heatmap.png,Variables SepalWidthCm and SepalLengthCm are redundant.,7902
Iris_correlation_heatmap.png,Variables PetalLengthCm and SepalLengthCm are redundant.,7903
Iris_correlation_heatmap.png,Variables PetalWidthCm and SepalLengthCm are redundant.,7904
Iris_correlation_heatmap.png,Variables SepalLengthCm and SepalWidthCm are redundant.,7905
Iris_correlation_heatmap.png,Variables PetalLengthCm and SepalWidthCm are redundant.,7906
Iris_correlation_heatmap.png,Variables PetalWidthCm and SepalWidthCm are redundant.,7907
Iris_correlation_heatmap.png,Variables SepalLengthCm and PetalLengthCm are redundant.,7908
Iris_correlation_heatmap.png,Variables SepalWidthCm and PetalLengthCm are redundant.,7909
Iris_correlation_heatmap.png,Variables PetalWidthCm and PetalLengthCm are redundant.,7910
Iris_correlation_heatmap.png,Variables SepalLengthCm and PetalWidthCm are redundant.,7911
Iris_correlation_heatmap.png,Variables SepalWidthCm and PetalWidthCm are redundant.,7912
Iris_correlation_heatmap.png,Variables PetalLengthCm and PetalWidthCm are redundant.,7913
Iris_correlation_heatmap.png,"Variables SepalWidthCm and PetalLengthCm are redundant, but we can’t say the same for the pair SepalLengthCm and PetalWidthCm.",7914
Iris_correlation_heatmap.png,"Variables SepalLengthCm and PetalWidthCm are redundant, but we can’t say the same for the pair SepalWidthCm and PetalLengthCm.",7915
Iris_correlation_heatmap.png,"Variables PetalLengthCm and PetalWidthCm are redundant, but we can’t say the same for the pair SepalLengthCm and SepalWidthCm.",7916
Iris_correlation_heatmap.png,"Variables SepalWidthCm and PetalWidthCm are redundant, but we can’t say the same for the pair SepalLengthCm and PetalLengthCm.",7917
Iris_correlation_heatmap.png,"Variables SepalLengthCm and PetalLengthCm are redundant, but we can’t say the same for the pair SepalWidthCm and PetalWidthCm.",7918
Iris_correlation_heatmap.png,"Variables SepalLengthCm and SepalWidthCm are redundant, but we can’t say the same for the pair PetalLengthCm and PetalWidthCm.",7919
Iris_correlation_heatmap.png,The variable SepalLengthCm can be discarded without risking losing information.,7920
Iris_correlation_heatmap.png,The variable SepalWidthCm can be discarded without risking losing information.,7921
Iris_correlation_heatmap.png,The variable PetalLengthCm can be discarded without risking losing information.,7922
Iris_correlation_heatmap.png,The variable PetalWidthCm can be discarded without risking losing information.,7923
Iris_correlation_heatmap.png,One of the variables SepalWidthCm or SepalLengthCm can be discarded without losing information.,7924
Iris_correlation_heatmap.png,One of the variables PetalLengthCm or SepalLengthCm can be discarded without losing information.,7925
Iris_correlation_heatmap.png,One of the variables PetalWidthCm or SepalLengthCm can be discarded without losing information.,7926
Iris_correlation_heatmap.png,One of the variables SepalLengthCm or SepalWidthCm can be discarded without losing information.,7927
Iris_correlation_heatmap.png,One of the variables PetalLengthCm or SepalWidthCm can be discarded without losing information.,7928
Iris_correlation_heatmap.png,One of the variables PetalWidthCm or SepalWidthCm can be discarded without losing information.,7929
Iris_correlation_heatmap.png,One of the variables SepalLengthCm or PetalLengthCm can be discarded without losing information.,7930
Iris_correlation_heatmap.png,One of the variables SepalWidthCm or PetalLengthCm can be discarded without losing information.,7931
Iris_correlation_heatmap.png,One of the variables PetalWidthCm or PetalLengthCm can be discarded without losing information.,7932
Iris_correlation_heatmap.png,One of the variables SepalLengthCm or PetalWidthCm can be discarded without losing information.,7933
Iris_correlation_heatmap.png,One of the variables SepalWidthCm or PetalWidthCm can be discarded without losing information.,7934
Iris_correlation_heatmap.png,One of the variables PetalLengthCm or PetalWidthCm can be discarded without losing information.,7935
Iris_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,7936
Iris_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,7937
Iris_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,7938
Iris_histograms_numeric.png,At least 50 of the variables present outliers.,7939
Iris_histograms_numeric.png,At least 60 of the variables present outliers.,7940
Iris_boxplots.png,At least 75 of the variables present outliers.,7941
Iris_histograms_numeric.png,At least 85 of the variables present outliers.,7942
Iris_boxplots.png,Variable SepalLengthCm presents some outliers.,7943
Iris_boxplots.png,Variable SepalWidthCm presents some outliers.,7944
Iris_histograms_numeric.png,Variable PetalLengthCm presents some outliers.,7945
Iris_histograms_numeric.png,Variable PetalWidthCm presents some outliers.,7946
Iris_histograms_numeric.png,Variable SepalLengthCm doesn’t have any outliers.,7947
Iris_boxplots.png,Variable SepalWidthCm doesn’t have any outliers.,7948
Iris_histograms_numeric.png,Variable PetalLengthCm doesn’t have any outliers.,7949
Iris_histograms_numeric.png,Variable PetalWidthCm doesn’t have any outliers.,7950
Iris_histograms_numeric.png,Variable SepalLengthCm shows some outlier values.,7951
Iris_histograms_numeric.png,Variable SepalWidthCm shows some outlier values.,7952
Iris_boxplots.png,Variable PetalLengthCm shows some outlier values.,7953
Iris_histograms_numeric.png,Variable PetalWidthCm shows some outlier values.,7954
Iris_boxplots.png,Variable SepalLengthCm shows a high number of outlier values.,7955
Iris_boxplots.png,Variable SepalWidthCm shows a high number of outlier values.,7956
Iris_boxplots.png,Variable PetalLengthCm shows a high number of outlier values.,7957
Iris_histograms_numeric.png,Variable PetalWidthCm shows a high number of outlier values.,7958
Iris_boxplots.png,Outliers seem to be a problem in the dataset.,7959
Iris_histograms_numeric.png,"It is clear that variable SepalWidthCm shows some outliers, but we can’t be sure of the same for variable SepalLengthCm.",7960
Iris_boxplots.png,"It is clear that variable PetalLengthCm shows some outliers, but we can’t be sure of the same for variable SepalLengthCm.",7961
Iris_histograms_numeric.png,"It is clear that variable PetalWidthCm shows some outliers, but we can’t be sure of the same for variable SepalLengthCm.",7962
Iris_histograms_numeric.png,"It is clear that variable SepalLengthCm shows some outliers, but we can’t be sure of the same for variable SepalWidthCm.",7963
Iris_boxplots.png,"It is clear that variable PetalLengthCm shows some outliers, but we can’t be sure of the same for variable SepalWidthCm.",7964
Iris_boxplots.png,"It is clear that variable PetalWidthCm shows some outliers, but we can’t be sure of the same for variable SepalWidthCm.",7965
Iris_histograms_numeric.png,"It is clear that variable SepalLengthCm shows some outliers, but we can’t be sure of the same for variable PetalLengthCm.",7966
Iris_boxplots.png,"It is clear that variable SepalWidthCm shows some outliers, but we can’t be sure of the same for variable PetalLengthCm.",7967
Iris_boxplots.png,"It is clear that variable PetalWidthCm shows some outliers, but we can’t be sure of the same for variable PetalLengthCm.",7968
Iris_boxplots.png,"It is clear that variable SepalLengthCm shows some outliers, but we can’t be sure of the same for variable PetalWidthCm.",7969
Iris_boxplots.png,"It is clear that variable SepalWidthCm shows some outliers, but we can’t be sure of the same for variable PetalWidthCm.",7970
Iris_boxplots.png,"It is clear that variable PetalLengthCm shows some outliers, but we can’t be sure of the same for variable PetalWidthCm.",7971
Iris_boxplots.png,Those boxplots show that the data is not normalized.,7972
Iris_histograms_numeric.png,Variable SepalLengthCm is balanced.,7973
Iris_boxplots.png,Variable SepalWidthCm is balanced.,7974
Iris_histograms_numeric.png,Variable PetalLengthCm is balanced.,7975
Iris_histograms_numeric.png,Variable PetalWidthCm is balanced.,7976
Iris_histograms.png,The variable SepalLengthCm can be seen as ordinal without losing information.,7977
Iris_histograms.png,The variable SepalWidthCm can be seen as ordinal without losing information.,7978
Iris_histograms.png,The variable PetalLengthCm can be seen as ordinal without losing information.,7979
Iris_histograms.png,The variable PetalWidthCm can be seen as ordinal without losing information.,7980
Iris_histograms.png,The variable SepalLengthCm can be seen as ordinal.,7981
Iris_histograms.png,The variable SepalWidthCm can be seen as ordinal.,7982
Iris_histograms.png,The variable PetalLengthCm can be seen as ordinal.,7983
Iris_histograms.png,The variable PetalWidthCm can be seen as ordinal.,7984
Iris_histograms.png,"All variables, but the class, should be dealt with as numeric.",7985
Iris_histograms.png,"All variables, but the class, should be dealt with as binary.",7986
Iris_histograms.png,"All variables, but the class, should be dealt with as date.",7987
Iris_histograms.png,"All variables, but the class, should be dealt with as symbolic.",7988
Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 100.,7989
Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 91.,7990
Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 44.,7991
Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 21.,7992
Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 29.,7993
Iris_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,7994
Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",7995
Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",7996
Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",7997
Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",7998
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",7999
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",8000
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",8001
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",8002
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A,B), as 2.",8003
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 2.",8004
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 2.",8005
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 2.",8006
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A,B), as 3.",8007
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 3.",8008
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 3.",8009
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 3.",8010
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 49.",8011
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 49.",8012
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 49.",8013
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 49.",8014
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 49.",8015
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 49.",8016
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 49.",8017
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 49.",8018
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 49.",8019
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 49.",8020
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 49.",8021
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 49.",8022
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 12.",8023
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 12.",8024
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 12.",8025
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 12.",8026
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 12.",8027
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 12.",8028
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 12.",8029
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 12.",8030
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 12.",8031
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 12.",8032
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 12.",8033
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 12.",8034
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 60.",8035
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 60.",8036
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 60.",8037
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 60.",8038
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 60.",8039
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 60.",8040
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 60.",8041
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 60.",8042
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 60.",8043
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 60.",8044
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 60.",8045
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 60.",8046
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (A,B) as 1.",8047
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, B) as 1.",8048
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (A, not B) as 1.",8049
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, not B) as 1.",8050
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (A,B) as 2.",8051
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, B) as 2.",8052
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (A, not B) as 2.",8053
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, not B) as 2.",8054
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (A,B) as 3.",8055
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, B) as 3.",8056
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (A, not B) as 3.",8057
Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, not B) as 3.",8058
Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 948 episodes.,8059
Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1389 episodes.,8060
Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 702 episodes.,8061
Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1406 episodes.,8062
Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 631 episodes.,8063
Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 99 estimators.,8064
Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 128 estimators.,8065
Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 73 estimators.,8066
Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 129 estimators.,8067
Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 101 estimators.,8068
Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 59 estimators.,8069
Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 147 estimators.,8070
Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 113 estimators.,8071
Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 75 estimators.,8072
Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 55 estimators.,8073
Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,8074
Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,8075
Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,8076
Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,8077
Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,8078
Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,8079
Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,8080
Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,8081
Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,8082
Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,8083
Wine_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,8084
Wine_decision_tree.png,The recall for the presented tree is higher than its accuracy.,8085
Wine_decision_tree.png,The precision for the presented tree is higher than its accuracy.,8086
Wine_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,8087
Wine_decision_tree.png,The recall for the presented tree is lower than its accuracy.,8088
Wine_decision_tree.png,The precision for the presented tree is lower than its accuracy.,8089
Wine_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,8090
Wine_decision_tree.png,The accuracy for the presented tree is higher than its recall.,8091
Wine_decision_tree.png,The precision for the presented tree is higher than its recall.,8092
Wine_decision_tree.png,The specificity for the presented tree is higher than its recall.,8093
Wine_decision_tree.png,The accuracy for the presented tree is lower than its recall.,8094
Wine_decision_tree.png,The precision for the presented tree is lower than its recall.,8095
Wine_decision_tree.png,The specificity for the presented tree is lower than its recall.,8096
Wine_decision_tree.png,The accuracy for the presented tree is higher than its precision.,8097
Wine_decision_tree.png,The recall for the presented tree is higher than its precision.,8098
Wine_decision_tree.png,The specificity for the presented tree is higher than its precision.,8099
Wine_decision_tree.png,The accuracy for the presented tree is lower than its precision.,8100
Wine_decision_tree.png,The recall for the presented tree is lower than its precision.,8101
Wine_decision_tree.png,The specificity for the presented tree is lower than its precision.,8102
Wine_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,8103
Wine_decision_tree.png,The recall for the presented tree is higher than its specificity.,8104
Wine_decision_tree.png,The precision for the presented tree is higher than its specificity.,8105
Wine_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,8106
Wine_decision_tree.png,The recall for the presented tree is lower than its specificity.,8107
Wine_decision_tree.png,The precision for the presented tree is lower than its specificity.,8108
Wine_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,8109
Wine_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,8110
Wine_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,8111
Wine_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,8112
Wine_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,8113
Wine_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,8114
Wine_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,8115
Wine_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,8116
Wine_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,8117
Wine_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,8118
Wine_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,8119
Wine_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,8120
Wine_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,8121
Wine_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,8122
Wine_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,8123
Wine_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,8124
Wine_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,8125
Wine_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,8126
Wine_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,8127
Wine_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,8128
Wine_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,8129
Wine_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,8130
Wine_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,8131
Wine_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,8132
Wine_decision_tree.png,The number of True Positives reported in the same tree is 20.,8133
Wine_decision_tree.png,The number of False Positives reported in the same tree is 45.,8134
Wine_decision_tree.png,The number of True Negatives reported in the same tree is 44.,8135
Wine_decision_tree.png,The number of False Negatives reported in the same tree is 29.,8136
Wine_decision_tree.png,The number of True Positives reported in the same tree is 23.,8137
Wine_decision_tree.png,The number of False Positives reported in the same tree is 13.,8138
Wine_decision_tree.png,The number of True Negatives reported in the same tree is 12.,8139
Wine_decision_tree.png,The number of False Negatives reported in the same tree is 36.,8140
Wine_decision_tree.png,The number of True Positives reported in the same tree is 50.,8141
Wine_decision_tree.png,The number of False Positives reported in the same tree is 42.,8142
Wine_decision_tree.png,The number of True Negatives reported in the same tree is 38.,8143
Wine_decision_tree.png,The number of False Negatives reported in the same tree is 39.,8144
Wine_decision_tree.png,The number of True Positives reported in the same tree is 32.,8145
Wine_decision_tree.png,The number of False Positives reported in the same tree is 17.,8146
Wine_decision_tree.png,The number of True Negatives reported in the same tree is 35.,8147
Wine_decision_tree.png,The number of False Negatives reported in the same tree is 46.,8148
Wine_decision_tree.png,The number of True Positives reported in the same tree is 34.,8149
Wine_decision_tree.png,The number of False Positives reported in the same tree is 43.,8150
Wine_decision_tree.png,The number of True Negatives reported in the same tree is 27.,8151
Wine_decision_tree.png,The number of False Negatives reported in the same tree is 47.,8152
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,8153
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,8154
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,8155
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,8156
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,8157
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,8158
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,8159
Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,8160
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,8161
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,8162
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,8163
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,8164
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,8165
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,8166
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,8167
Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,8168
Wine_decision_tree.png,The accuracy for the presented tree is higher than 78%.,8169
Wine_decision_tree.png,The recall for the presented tree is higher than 70%.,8170
Wine_decision_tree.png,The precision for the presented tree is higher than 73%.,8171
Wine_decision_tree.png,The specificity for the presented tree is higher than 71%.,8172
Wine_decision_tree.png,The accuracy for the presented tree is lower than 76%.,8173
Wine_decision_tree.png,The recall for the presented tree is lower than 86%.,8174
Wine_decision_tree.png,The precision for the presented tree is lower than 62%.,8175
Wine_decision_tree.png,The specificity for the presented tree is lower than 65%.,8176
Wine_decision_tree.png,The accuracy for the presented tree is higher than 72%.,8177
Wine_decision_tree.png,The recall for the presented tree is higher than 77%.,8178
Wine_decision_tree.png,The precision for the presented tree is higher than 83%.,8179
Wine_decision_tree.png,The specificity for the presented tree is higher than 69%.,8180
Wine_decision_tree.png,The accuracy for the presented tree is lower than 74%.,8181
Wine_decision_tree.png,The recall for the presented tree is lower than 81%.,8182
Wine_decision_tree.png,The precision for the presented tree is lower than 87%.,8183
Wine_decision_tree.png,The specificity for the presented tree is lower than 67%.,8184
Wine_decision_tree.png,The accuracy for the presented tree is higher than 60%.,8185
Wine_decision_tree.png,The recall for the presented tree is higher than 66%.,8186
Wine_decision_tree.png,The precision for the presented tree is higher than 79%.,8187
Wine_decision_tree.png,The specificity for the presented tree is higher than 68%.,8188
Wine_decision_tree.png,The accuracy for the presented tree is lower than 64%.,8189
Wine_decision_tree.png,The recall for the presented tree is lower than 88%.,8190
Wine_decision_tree.png,The precision for the presented tree is lower than 89%.,8191
Wine_decision_tree.png,The specificity for the presented tree is lower than 80%.,8192
Wine_decision_tree.png,The accuracy for the presented tree is higher than 63%.,8193
Wine_decision_tree.png,The recall for the presented tree is higher than 85%.,8194
Wine_decision_tree.png,The precision for the presented tree is higher than 90%.,8195
Wine_decision_tree.png,The specificity for the presented tree is higher than 75%.,8196
Wine_decision_tree.png,The accuracy for the presented tree is lower than 84%.,8197
Wine_decision_tree.png,The recall for the presented tree is lower than 82%.,8198
Wine_decision_tree.png,The precision for the presented tree is lower than 61%.,8199
Wine_decision_tree.png,The specificity for the presented tree is lower than 75%.,8200
Wine_decision_tree.png,The accuracy for the presented tree is higher than 85%.,8201
Wine_decision_tree.png,The recall for the presented tree is higher than 84%.,8202
Wine_decision_tree.png,The precision for the presented tree is higher than 82%.,8203
Wine_decision_tree.png,The specificity for the presented tree is higher than 62%.,8204
Wine_decision_tree.png,The accuracy for the presented tree is lower than 80%.,8205
Wine_decision_tree.png,The recall for the presented tree is lower than 80%.,8206
Wine_decision_tree.png,The precision for the presented tree is lower than 87%.,8207
Wine_decision_tree.png,The specificity for the presented tree is lower than 69%.,8208
Wine_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",8209
Wine_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",8210
Wine_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",8211
Wine_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",8212
Wine_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",8213
Wine_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",8214
Wine_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,8215
Wine_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,8216
Wine_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,8217
Wine_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,8218
Wine_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,8219
Wine_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,8220
Wine_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,8221
Wine_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,8222
Wine_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,8223
Wine_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,8224
Wine_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,8225
Wine_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,8226
Wine_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,8227
Wine_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,8228
Wine_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,8229
Wine_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,8230
Wine_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,8231
Wine_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,8232
Wine_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,8233
Wine_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,8234
Wine_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,8235
Wine_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,8236
Wine_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,8237
Wine_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,8238
Wine_overfitting_knn.png,KNN is in overfitting for k less than 2.,8239
Wine_overfitting_knn.png,KNN is in overfitting for k larger than 2.,8240
Wine_overfitting_knn.png,KNN is in overfitting for k less than 3.,8241
Wine_overfitting_knn.png,KNN is in overfitting for k larger than 3.,8242
Wine_overfitting_knn.png,KNN is in overfitting for k less than 4.,8243
Wine_overfitting_knn.png,KNN is in overfitting for k larger than 4.,8244
Wine_overfitting_knn.png,KNN is in overfitting for k less than 5.,8245
Wine_overfitting_knn.png,KNN is in overfitting for k larger than 5.,8246
Wine_overfitting_knn.png,KNN is in overfitting for k less than 6.,8247
Wine_overfitting_knn.png,KNN is in overfitting for k larger than 6.,8248
Wine_overfitting_knn.png,KNN is in overfitting for k less than 7.,8249
Wine_overfitting_knn.png,KNN is in overfitting for k larger than 7.,8250
Wine_overfitting_knn.png,KNN is in overfitting for k less than 8.,8251
Wine_overfitting_knn.png,KNN is in overfitting for k larger than 8.,8252
Wine_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",8253
Wine_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",8254
Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",8255
Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",8256
Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",8257
Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",8258
Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",8259
Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",8260
Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",8261
Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",8262
Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",8263
Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",8264
Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",8265
Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",8266
Wine_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,8267
Wine_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,8268
Wine_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,8269
Wine_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,8270
Wine_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,8271
Wine_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,8272
Wine_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,8273
Wine_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,8274
Wine_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,8275
Wine_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,8276
Wine_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,8277
Wine_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,8278
Wine_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,8279
Wine_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,8280
Wine_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,8281
Wine_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,8282
Wine_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,8283
Wine_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,8284
Wine_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,8285
Wine_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,8286
Wine_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,8287
Wine_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,8288
Wine_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,8289
Wine_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,8290
Wine_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,8291
Wine_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,8292
Wine_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,8293
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Class.,8294
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Class.,8295
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Class.,8296
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Class.,8297
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Class.,8298
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Class.,8299
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Class.,8300
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Class.,8301
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Class.,8302
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Class.,8303
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Class.,8304
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Alcohol.,8305
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Alcohol.,8306
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Alcohol.,8307
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Alcohol.,8308
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Alcohol.,8309
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Alcohol.,8310
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Alcohol.,8311
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Alcohol.,8312
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Alcohol.,8313
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Alcohol.,8314
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Alcohol.,8315
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Malic acid.,8316
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Malic acid.,8317
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Malic acid.,8318
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Malic acid.,8319
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Malic acid.,8320
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Malic acid.,8321
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Malic acid.,8322
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Malic acid.,8323
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Malic acid.,8324
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Malic acid.,8325
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Malic acid.,8326
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Ash.,8327
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Ash.,8328
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Ash.,8329
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Ash.,8330
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Ash.,8331
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Ash.,8332
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Ash.,8333
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Ash.,8334
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Ash.,8335
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Ash.,8336
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Ash.,8337
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Alcalinity of ash.,8338
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Alcalinity of ash.,8339
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Alcalinity of ash.,8340
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Alcalinity of ash.,8341
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Alcalinity of ash.,8342
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Alcalinity of ash.,8343
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Alcalinity of ash.,8344
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Alcalinity of ash.,8345
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Alcalinity of ash.,8346
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Alcalinity of ash.,8347
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Alcalinity of ash.,8348
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Total phenols.,8349
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Total phenols.,8350
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Total phenols.,8351
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Total phenols.,8352
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Total phenols.,8353
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Total phenols.,8354
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Total phenols.,8355
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Total phenols.,8356
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Total phenols.,8357
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Total phenols.,8358
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Total phenols.,8359
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Flavanoids.,8360
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Flavanoids.,8361
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Flavanoids.,8362
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Flavanoids.,8363
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Flavanoids.,8364
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Flavanoids.,8365
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Flavanoids.,8366
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Flavanoids.,8367
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Flavanoids.,8368
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Flavanoids.,8369
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Flavanoids.,8370
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Nonflavanoid phenols.,8371
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Nonflavanoid phenols.,8372
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Nonflavanoid phenols.,8373
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Nonflavanoid phenols.,8374
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Nonflavanoid phenols.,8375
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Nonflavanoid phenols.,8376
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Nonflavanoid phenols.,8377
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Nonflavanoid phenols.,8378
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Nonflavanoid phenols.,8379
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Nonflavanoid phenols.,8380
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Nonflavanoid phenols.,8381
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Proanthocyanins.,8382
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Proanthocyanins.,8383
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Proanthocyanins.,8384
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Proanthocyanins.,8385
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Proanthocyanins.,8386
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Proanthocyanins.,8387
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Proanthocyanins.,8388
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Proanthocyanins.,8389
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Proanthocyanins.,8390
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Proanthocyanins.,8391
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Proanthocyanins.,8392
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Color intensity.,8393
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Color intensity.,8394
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Color intensity.,8395
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Color intensity.,8396
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Color intensity.,8397
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Color intensity.,8398
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Color intensity.,8399
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Color intensity.,8400
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Color intensity.,8401
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Color intensity.,8402
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Color intensity.,8403
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Hue.,8404
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Hue.,8405
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Hue.,8406
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Hue.,8407
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Hue.,8408
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Hue.,8409
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Hue.,8410
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Hue.,8411
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Hue.,8412
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Hue.,8413
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Hue.,8414
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable OD280-OD315 of diluted wines.,8415
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable OD280-OD315 of diluted wines.,8416
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable OD280-OD315 of diluted wines.,8417
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable OD280-OD315 of diluted wines.,8418
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable OD280-OD315 of diluted wines.,8419
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable OD280-OD315 of diluted wines.,8420
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable OD280-OD315 of diluted wines.,8421
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable OD280-OD315 of diluted wines.,8422
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable OD280-OD315 of diluted wines.,8423
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable OD280-OD315 of diluted wines.,8424
Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable OD280-OD315 of diluted wines.,8425
Wine_pca.png,The first 2 principal components are enough for explaining half the data variance.,8426
Wine_pca.png,The first 3 principal components are enough for explaining half the data variance.,8427
Wine_pca.png,The first 4 principal components are enough for explaining half the data variance.,8428
Wine_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,8429
Wine_correlation_heatmap.png,Removing variable Class might improve the training of decision trees .,8430
Wine_correlation_heatmap.png,Removing variable Alcohol might improve the training of decision trees .,8431
Wine_correlation_heatmap.png,Removing variable Malic acid might improve the training of decision trees .,8432
Wine_correlation_heatmap.png,Removing variable Ash might improve the training of decision trees .,8433
Wine_correlation_heatmap.png,Removing variable Alcalinity of ash might improve the training of decision trees .,8434
Wine_correlation_heatmap.png,Removing variable Total phenols might improve the training of decision trees .,8435
Wine_correlation_heatmap.png,Removing variable Flavanoids might improve the training of decision trees .,8436
Wine_correlation_heatmap.png,Removing variable Nonflavanoid phenols might improve the training of decision trees .,8437
Wine_correlation_heatmap.png,Removing variable Proanthocyanins might improve the training of decision trees .,8438
Wine_correlation_heatmap.png,Removing variable Color intensity might improve the training of decision trees .,8439
Wine_correlation_heatmap.png,Removing variable Hue might improve the training of decision trees .,8440
Wine_correlation_heatmap.png,Removing variable OD280-OD315 of diluted wines might improve the training of decision trees .,8441
Wine_histograms.png,"Not knowing the semantics of Alcohol variable, dummification could have been a more adequate codification.",8442
Wine_histograms.png,"Not knowing the semantics of Malic acid variable, dummification could have been a more adequate codification.",8443
Wine_histograms.png,"Not knowing the semantics of Ash variable, dummification could have been a more adequate codification.",8444
Wine_histograms.png,"Not knowing the semantics of Alcalinity of ash variable, dummification could have been a more adequate codification.",8445
Wine_histograms.png,"Not knowing the semantics of Total phenols variable, dummification could have been a more adequate codification.",8446
Wine_histograms.png,"Not knowing the semantics of Flavanoids variable, dummification could have been a more adequate codification.",8447
Wine_histograms.png,"Not knowing the semantics of Nonflavanoid phenols variable, dummification could have been a more adequate codification.",8448
Wine_histograms.png,"Not knowing the semantics of Proanthocyanins variable, dummification could have been a more adequate codification.",8449
Wine_histograms.png,"Not knowing the semantics of Color intensity variable, dummification could have been a more adequate codification.",8450
Wine_histograms.png,"Not knowing the semantics of Hue variable, dummification could have been a more adequate codification.",8451
Wine_histograms.png,"Not knowing the semantics of OD280-OD315 of diluted wines variable, dummification could have been a more adequate codification.",8452
Wine_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,8453
Wine_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",8454
Wine_histograms.png,"Given the usual semantics of Alcohol variable, dummification would have been a better codification.",8455
Wine_histograms.png,"Given the usual semantics of Malic acid variable, dummification would have been a better codification.",8456
Wine_histograms.png,"Given the usual semantics of Ash variable, dummification would have been a better codification.",8457
Wine_histograms.png,"Given the usual semantics of Alcalinity of ash variable, dummification would have been a better codification.",8458
Wine_histograms.png,"Given the usual semantics of Total phenols variable, dummification would have been a better codification.",8459
Wine_histograms.png,"Given the usual semantics of Flavanoids variable, dummification would have been a better codification.",8460
Wine_histograms.png,"Given the usual semantics of Nonflavanoid phenols variable, dummification would have been a better codification.",8461
Wine_histograms.png,"Given the usual semantics of Proanthocyanins variable, dummification would have been a better codification.",8462
Wine_histograms.png,"Given the usual semantics of Color intensity variable, dummification would have been a better codification.",8463
Wine_histograms.png,"Given the usual semantics of Hue variable, dummification would have been a better codification.",8464
Wine_histograms.png,"Given the usual semantics of OD280-OD315 of diluted wines variable, dummification would have been a better codification.",8465
Wine_histograms.png,The variable Alcohol can be coded as ordinal without losing information.,8466
Wine_histograms.png,The variable Malic acid can be coded as ordinal without losing information.,8467
Wine_histograms.png,The variable Ash can be coded as ordinal without losing information.,8468
Wine_histograms.png,The variable Alcalinity of ash can be coded as ordinal without losing information.,8469
Wine_histograms.png,The variable Total phenols can be coded as ordinal without losing information.,8470
Wine_histograms.png,The variable Flavanoids can be coded as ordinal without losing information.,8471
Wine_histograms.png,The variable Nonflavanoid phenols can be coded as ordinal without losing information.,8472
Wine_histograms.png,The variable Proanthocyanins can be coded as ordinal without losing information.,8473
Wine_histograms.png,The variable Color intensity can be coded as ordinal without losing information.,8474
Wine_histograms.png,The variable Hue can be coded as ordinal without losing information.,8475
Wine_histograms.png,The variable OD280-OD315 of diluted wines can be coded as ordinal without losing information.,8476
Wine_histograms.png,"Considering the common semantics for Alcohol variable, dummification would be the most adequate encoding.",8477
Wine_histograms.png,"Considering the common semantics for Malic acid variable, dummification would be the most adequate encoding.",8478
Wine_histograms.png,"Considering the common semantics for Ash variable, dummification would be the most adequate encoding.",8479
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash variable, dummification would be the most adequate encoding.",8480
Wine_histograms.png,"Considering the common semantics for Total phenols variable, dummification would be the most adequate encoding.",8481
Wine_histograms.png,"Considering the common semantics for Flavanoids variable, dummification would be the most adequate encoding.",8482
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols variable, dummification would be the most adequate encoding.",8483
Wine_histograms.png,"Considering the common semantics for Proanthocyanins variable, dummification would be the most adequate encoding.",8484
Wine_histograms.png,"Considering the common semantics for Color intensity variable, dummification would be the most adequate encoding.",8485
Wine_histograms.png,"Considering the common semantics for Hue variable, dummification would be the most adequate encoding.",8486
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines variable, dummification would be the most adequate encoding.",8487
Wine_histograms.png,"Considering the common semantics for Malic acid and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8488
Wine_histograms.png,"Considering the common semantics for Ash and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8489
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8490
Wine_histograms.png,"Considering the common semantics for Total phenols and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8491
Wine_histograms.png,"Considering the common semantics for Flavanoids and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8492
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8493
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8494
Wine_histograms.png,"Considering the common semantics for Color intensity and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8495
Wine_histograms.png,"Considering the common semantics for Hue and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8496
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8497
Wine_histograms.png,"Considering the common semantics for Alcohol and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8498
Wine_histograms.png,"Considering the common semantics for Ash and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8499
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8500
Wine_histograms.png,"Considering the common semantics for Total phenols and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8501
Wine_histograms.png,"Considering the common semantics for Flavanoids and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8502
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8503
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8504
Wine_histograms.png,"Considering the common semantics for Color intensity and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8505
Wine_histograms.png,"Considering the common semantics for Hue and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8506
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8507
Wine_histograms.png,"Considering the common semantics for Alcohol and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8508
Wine_histograms.png,"Considering the common semantics for Malic acid and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8509
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8510
Wine_histograms.png,"Considering the common semantics for Total phenols and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8511
Wine_histograms.png,"Considering the common semantics for Flavanoids and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8512
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8513
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8514
Wine_histograms.png,"Considering the common semantics for Color intensity and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8515
Wine_histograms.png,"Considering the common semantics for Hue and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8516
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8517
Wine_histograms.png,"Considering the common semantics for Alcohol and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8518
Wine_histograms.png,"Considering the common semantics for Malic acid and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8519
Wine_histograms.png,"Considering the common semantics for Ash and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8520
Wine_histograms.png,"Considering the common semantics for Total phenols and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8521
Wine_histograms.png,"Considering the common semantics for Flavanoids and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8522
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8523
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8524
Wine_histograms.png,"Considering the common semantics for Color intensity and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8525
Wine_histograms.png,"Considering the common semantics for Hue and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8526
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8527
Wine_histograms.png,"Considering the common semantics for Alcohol and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8528
Wine_histograms.png,"Considering the common semantics for Malic acid and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8529
Wine_histograms.png,"Considering the common semantics for Ash and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8530
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8531
Wine_histograms.png,"Considering the common semantics for Flavanoids and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8532
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8533
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8534
Wine_histograms.png,"Considering the common semantics for Color intensity and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8535
Wine_histograms.png,"Considering the common semantics for Hue and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8536
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8537
Wine_histograms.png,"Considering the common semantics for Alcohol and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8538
Wine_histograms.png,"Considering the common semantics for Malic acid and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8539
Wine_histograms.png,"Considering the common semantics for Ash and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8540
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8541
Wine_histograms.png,"Considering the common semantics for Total phenols and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8542
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8543
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8544
Wine_histograms.png,"Considering the common semantics for Color intensity and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8545
Wine_histograms.png,"Considering the common semantics for Hue and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8546
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8547
Wine_histograms.png,"Considering the common semantics for Alcohol and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8548
Wine_histograms.png,"Considering the common semantics for Malic acid and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8549
Wine_histograms.png,"Considering the common semantics for Ash and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8550
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8551
Wine_histograms.png,"Considering the common semantics for Total phenols and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8552
Wine_histograms.png,"Considering the common semantics for Flavanoids and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8553
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8554
Wine_histograms.png,"Considering the common semantics for Color intensity and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8555
Wine_histograms.png,"Considering the common semantics for Hue and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8556
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Nonflavanoid phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8557
Wine_histograms.png,"Considering the common semantics for Alcohol and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8558
Wine_histograms.png,"Considering the common semantics for Malic acid and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8559
Wine_histograms.png,"Considering the common semantics for Ash and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8560
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8561
Wine_histograms.png,"Considering the common semantics for Total phenols and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8562
Wine_histograms.png,"Considering the common semantics for Flavanoids and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8563
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8564
Wine_histograms.png,"Considering the common semantics for Color intensity and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8565
Wine_histograms.png,"Considering the common semantics for Hue and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8566
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8567
Wine_histograms.png,"Considering the common semantics for Alcohol and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8568
Wine_histograms.png,"Considering the common semantics for Malic acid and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8569
Wine_histograms.png,"Considering the common semantics for Ash and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8570
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8571
Wine_histograms.png,"Considering the common semantics for Total phenols and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8572
Wine_histograms.png,"Considering the common semantics for Flavanoids and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8573
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8574
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8575
Wine_histograms.png,"Considering the common semantics for Hue and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8576
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8577
Wine_histograms.png,"Considering the common semantics for Alcohol and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8578
Wine_histograms.png,"Considering the common semantics for Malic acid and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8579
Wine_histograms.png,"Considering the common semantics for Ash and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8580
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8581
Wine_histograms.png,"Considering the common semantics for Total phenols and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8582
Wine_histograms.png,"Considering the common semantics for Flavanoids and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8583
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8584
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8585
Wine_histograms.png,"Considering the common semantics for Color intensity and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8586
Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8587
Wine_histograms.png,"Considering the common semantics for Alcohol and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8588
Wine_histograms.png,"Considering the common semantics for Malic acid and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8589
Wine_histograms.png,"Considering the common semantics for Ash and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8590
Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8591
Wine_histograms.png,"Considering the common semantics for Total phenols and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8592
Wine_histograms.png,"Considering the common semantics for Flavanoids and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8593
Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8594
Wine_histograms.png,"Considering the common semantics for Proanthocyanins and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8595
Wine_histograms.png,"Considering the common semantics for Color intensity and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8596
Wine_histograms.png,"Considering the common semantics for Hue and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8597
Wine_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,8598
Wine_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,8599
Wine_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,8600
Wine_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",8601
Wine_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",8602
Wine_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",8603
Wine_correlation_heatmap.png,Variables Alcohol and Class seem to be useful for classification tasks.,8604
Wine_correlation_heatmap.png,Variables Malic acid and Class seem to be useful for classification tasks.,8605
Wine_correlation_heatmap.png,Variables Ash and Class seem to be useful for classification tasks.,8606
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Class seem to be useful for classification tasks.,8607
Wine_correlation_heatmap.png,Variables Total phenols and Class seem to be useful for classification tasks.,8608
Wine_correlation_heatmap.png,Variables Flavanoids and Class seem to be useful for classification tasks.,8609
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Class seem to be useful for classification tasks.,8610
Wine_correlation_heatmap.png,Variables Proanthocyanins and Class seem to be useful for classification tasks.,8611
Wine_correlation_heatmap.png,Variables Color intensity and Class seem to be useful for classification tasks.,8612
Wine_correlation_heatmap.png,Variables Hue and Class seem to be useful for classification tasks.,8613
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Class seem to be useful for classification tasks.,8614
Wine_correlation_heatmap.png,Variables Class and Alcohol seem to be useful for classification tasks.,8615
Wine_correlation_heatmap.png,Variables Malic acid and Alcohol seem to be useful for classification tasks.,8616
Wine_correlation_heatmap.png,Variables Ash and Alcohol seem to be useful for classification tasks.,8617
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Alcohol seem to be useful for classification tasks.,8618
Wine_correlation_heatmap.png,Variables Total phenols and Alcohol seem to be useful for classification tasks.,8619
Wine_correlation_heatmap.png,Variables Flavanoids and Alcohol seem to be useful for classification tasks.,8620
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Alcohol seem to be useful for classification tasks.,8621
Wine_correlation_heatmap.png,Variables Proanthocyanins and Alcohol seem to be useful for classification tasks.,8622
Wine_correlation_heatmap.png,Variables Color intensity and Alcohol seem to be useful for classification tasks.,8623
Wine_correlation_heatmap.png,Variables Hue and Alcohol seem to be useful for classification tasks.,8624
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Alcohol seem to be useful for classification tasks.,8625
Wine_correlation_heatmap.png,Variables Class and Malic acid seem to be useful for classification tasks.,8626
Wine_correlation_heatmap.png,Variables Alcohol and Malic acid seem to be useful for classification tasks.,8627
Wine_correlation_heatmap.png,Variables Ash and Malic acid seem to be useful for classification tasks.,8628
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Malic acid seem to be useful for classification tasks.,8629
Wine_correlation_heatmap.png,Variables Total phenols and Malic acid seem to be useful for classification tasks.,8630
Wine_correlation_heatmap.png,Variables Flavanoids and Malic acid seem to be useful for classification tasks.,8631
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Malic acid seem to be useful for classification tasks.,8632
Wine_correlation_heatmap.png,Variables Proanthocyanins and Malic acid seem to be useful for classification tasks.,8633
Wine_correlation_heatmap.png,Variables Color intensity and Malic acid seem to be useful for classification tasks.,8634
Wine_correlation_heatmap.png,Variables Hue and Malic acid seem to be useful for classification tasks.,8635
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Malic acid seem to be useful for classification tasks.,8636
Wine_correlation_heatmap.png,Variables Class and Ash seem to be useful for classification tasks.,8637
Wine_correlation_heatmap.png,Variables Alcohol and Ash seem to be useful for classification tasks.,8638
Wine_correlation_heatmap.png,Variables Malic acid and Ash seem to be useful for classification tasks.,8639
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Ash seem to be useful for classification tasks.,8640
Wine_correlation_heatmap.png,Variables Total phenols and Ash seem to be useful for classification tasks.,8641
Wine_correlation_heatmap.png,Variables Flavanoids and Ash seem to be useful for classification tasks.,8642
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Ash seem to be useful for classification tasks.,8643
Wine_correlation_heatmap.png,Variables Proanthocyanins and Ash seem to be useful for classification tasks.,8644
Wine_correlation_heatmap.png,Variables Color intensity and Ash seem to be useful for classification tasks.,8645
Wine_correlation_heatmap.png,Variables Hue and Ash seem to be useful for classification tasks.,8646
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Ash seem to be useful for classification tasks.,8647
Wine_correlation_heatmap.png,Variables Class and Alcalinity of ash seem to be useful for classification tasks.,8648
Wine_correlation_heatmap.png,Variables Alcohol and Alcalinity of ash seem to be useful for classification tasks.,8649
Wine_correlation_heatmap.png,Variables Malic acid and Alcalinity of ash seem to be useful for classification tasks.,8650
Wine_correlation_heatmap.png,Variables Ash and Alcalinity of ash seem to be useful for classification tasks.,8651
Wine_correlation_heatmap.png,Variables Total phenols and Alcalinity of ash seem to be useful for classification tasks.,8652
Wine_correlation_heatmap.png,Variables Flavanoids and Alcalinity of ash seem to be useful for classification tasks.,8653
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Alcalinity of ash seem to be useful for classification tasks.,8654
Wine_correlation_heatmap.png,Variables Proanthocyanins and Alcalinity of ash seem to be useful for classification tasks.,8655
Wine_correlation_heatmap.png,Variables Color intensity and Alcalinity of ash seem to be useful for classification tasks.,8656
Wine_correlation_heatmap.png,Variables Hue and Alcalinity of ash seem to be useful for classification tasks.,8657
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Alcalinity of ash seem to be useful for classification tasks.,8658
Wine_correlation_heatmap.png,Variables Class and Total phenols seem to be useful for classification tasks.,8659
Wine_correlation_heatmap.png,Variables Alcohol and Total phenols seem to be useful for classification tasks.,8660
Wine_correlation_heatmap.png,Variables Malic acid and Total phenols seem to be useful for classification tasks.,8661
Wine_correlation_heatmap.png,Variables Ash and Total phenols seem to be useful for classification tasks.,8662
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Total phenols seem to be useful for classification tasks.,8663
Wine_correlation_heatmap.png,Variables Flavanoids and Total phenols seem to be useful for classification tasks.,8664
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Total phenols seem to be useful for classification tasks.,8665
Wine_correlation_heatmap.png,Variables Proanthocyanins and Total phenols seem to be useful for classification tasks.,8666
Wine_correlation_heatmap.png,Variables Color intensity and Total phenols seem to be useful for classification tasks.,8667
Wine_correlation_heatmap.png,Variables Hue and Total phenols seem to be useful for classification tasks.,8668
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Total phenols seem to be useful for classification tasks.,8669
Wine_correlation_heatmap.png,Variables Class and Flavanoids seem to be useful for classification tasks.,8670
Wine_correlation_heatmap.png,Variables Alcohol and Flavanoids seem to be useful for classification tasks.,8671
Wine_correlation_heatmap.png,Variables Malic acid and Flavanoids seem to be useful for classification tasks.,8672
Wine_correlation_heatmap.png,Variables Ash and Flavanoids seem to be useful for classification tasks.,8673
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Flavanoids seem to be useful for classification tasks.,8674
Wine_correlation_heatmap.png,Variables Total phenols and Flavanoids seem to be useful for classification tasks.,8675
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Flavanoids seem to be useful for classification tasks.,8676
Wine_correlation_heatmap.png,Variables Proanthocyanins and Flavanoids seem to be useful for classification tasks.,8677
Wine_correlation_heatmap.png,Variables Color intensity and Flavanoids seem to be useful for classification tasks.,8678
Wine_correlation_heatmap.png,Variables Hue and Flavanoids seem to be useful for classification tasks.,8679
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Flavanoids seem to be useful for classification tasks.,8680
Wine_correlation_heatmap.png,Variables Class and Nonflavanoid phenols seem to be useful for classification tasks.,8681
Wine_correlation_heatmap.png,Variables Alcohol and Nonflavanoid phenols seem to be useful for classification tasks.,8682
Wine_correlation_heatmap.png,Variables Malic acid and Nonflavanoid phenols seem to be useful for classification tasks.,8683
Wine_correlation_heatmap.png,Variables Ash and Nonflavanoid phenols seem to be useful for classification tasks.,8684
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Nonflavanoid phenols seem to be useful for classification tasks.,8685
Wine_correlation_heatmap.png,Variables Total phenols and Nonflavanoid phenols seem to be useful for classification tasks.,8686
Wine_correlation_heatmap.png,Variables Flavanoids and Nonflavanoid phenols seem to be useful for classification tasks.,8687
Wine_correlation_heatmap.png,Variables Proanthocyanins and Nonflavanoid phenols seem to be useful for classification tasks.,8688
Wine_correlation_heatmap.png,Variables Color intensity and Nonflavanoid phenols seem to be useful for classification tasks.,8689
Wine_correlation_heatmap.png,Variables Hue and Nonflavanoid phenols seem to be useful for classification tasks.,8690
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Nonflavanoid phenols seem to be useful for classification tasks.,8691
Wine_correlation_heatmap.png,Variables Class and Proanthocyanins seem to be useful for classification tasks.,8692
Wine_correlation_heatmap.png,Variables Alcohol and Proanthocyanins seem to be useful for classification tasks.,8693
Wine_correlation_heatmap.png,Variables Malic acid and Proanthocyanins seem to be useful for classification tasks.,8694
Wine_correlation_heatmap.png,Variables Ash and Proanthocyanins seem to be useful for classification tasks.,8695
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Proanthocyanins seem to be useful for classification tasks.,8696
Wine_correlation_heatmap.png,Variables Total phenols and Proanthocyanins seem to be useful for classification tasks.,8697
Wine_correlation_heatmap.png,Variables Flavanoids and Proanthocyanins seem to be useful for classification tasks.,8698
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Proanthocyanins seem to be useful for classification tasks.,8699
Wine_correlation_heatmap.png,Variables Color intensity and Proanthocyanins seem to be useful for classification tasks.,8700
Wine_correlation_heatmap.png,Variables Hue and Proanthocyanins seem to be useful for classification tasks.,8701
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Proanthocyanins seem to be useful for classification tasks.,8702
Wine_correlation_heatmap.png,Variables Class and Color intensity seem to be useful for classification tasks.,8703
Wine_correlation_heatmap.png,Variables Alcohol and Color intensity seem to be useful for classification tasks.,8704
Wine_correlation_heatmap.png,Variables Malic acid and Color intensity seem to be useful for classification tasks.,8705
Wine_correlation_heatmap.png,Variables Ash and Color intensity seem to be useful for classification tasks.,8706
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Color intensity seem to be useful for classification tasks.,8707
Wine_correlation_heatmap.png,Variables Total phenols and Color intensity seem to be useful for classification tasks.,8708
Wine_correlation_heatmap.png,Variables Flavanoids and Color intensity seem to be useful for classification tasks.,8709
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Color intensity seem to be useful for classification tasks.,8710
Wine_correlation_heatmap.png,Variables Proanthocyanins and Color intensity seem to be useful for classification tasks.,8711
Wine_correlation_heatmap.png,Variables Hue and Color intensity seem to be useful for classification tasks.,8712
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Color intensity seem to be useful for classification tasks.,8713
Wine_correlation_heatmap.png,Variables Class and Hue seem to be useful for classification tasks.,8714
Wine_correlation_heatmap.png,Variables Alcohol and Hue seem to be useful for classification tasks.,8715
Wine_correlation_heatmap.png,Variables Malic acid and Hue seem to be useful for classification tasks.,8716
Wine_correlation_heatmap.png,Variables Ash and Hue seem to be useful for classification tasks.,8717
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Hue seem to be useful for classification tasks.,8718
Wine_correlation_heatmap.png,Variables Total phenols and Hue seem to be useful for classification tasks.,8719
Wine_correlation_heatmap.png,Variables Flavanoids and Hue seem to be useful for classification tasks.,8720
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Hue seem to be useful for classification tasks.,8721
Wine_correlation_heatmap.png,Variables Proanthocyanins and Hue seem to be useful for classification tasks.,8722
Wine_correlation_heatmap.png,Variables Color intensity and Hue seem to be useful for classification tasks.,8723
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Hue seem to be useful for classification tasks.,8724
Wine_correlation_heatmap.png,Variables Class and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8725
Wine_correlation_heatmap.png,Variables Alcohol and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8726
Wine_correlation_heatmap.png,Variables Malic acid and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8727
Wine_correlation_heatmap.png,Variables Ash and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8728
Wine_correlation_heatmap.png,Variables Alcalinity of ash and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8729
Wine_correlation_heatmap.png,Variables Total phenols and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8730
Wine_correlation_heatmap.png,Variables Flavanoids and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8731
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8732
Wine_correlation_heatmap.png,Variables Proanthocyanins and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8733
Wine_correlation_heatmap.png,Variables Color intensity and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8734
Wine_correlation_heatmap.png,Variables Hue and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8735
Wine_correlation_heatmap.png,Variable Class seems to be relevant for the majority of mining tasks.,8736
Wine_correlation_heatmap.png,Variable Alcohol seems to be relevant for the majority of mining tasks.,8737
Wine_correlation_heatmap.png,Variable Malic acid seems to be relevant for the majority of mining tasks.,8738
Wine_correlation_heatmap.png,Variable Ash seems to be relevant for the majority of mining tasks.,8739
Wine_correlation_heatmap.png,Variable Alcalinity of ash seems to be relevant for the majority of mining tasks.,8740
Wine_correlation_heatmap.png,Variable Total phenols seems to be relevant for the majority of mining tasks.,8741
Wine_correlation_heatmap.png,Variable Flavanoids seems to be relevant for the majority of mining tasks.,8742
Wine_correlation_heatmap.png,Variable Nonflavanoid phenols seems to be relevant for the majority of mining tasks.,8743
Wine_correlation_heatmap.png,Variable Proanthocyanins seems to be relevant for the majority of mining tasks.,8744
Wine_correlation_heatmap.png,Variable Color intensity seems to be relevant for the majority of mining tasks.,8745
Wine_correlation_heatmap.png,Variable Hue seems to be relevant for the majority of mining tasks.,8746
Wine_correlation_heatmap.png,Variable OD280-OD315 of diluted wines seems to be relevant for the majority of mining tasks.,8747
Wine_decision_tree.png,Variable Class is one of the most relevant variables.,8748
Wine_decision_tree.png,Variable Alcohol is one of the most relevant variables.,8749
Wine_decision_tree.png,Variable Malic acid is one of the most relevant variables.,8750
Wine_decision_tree.png,Variable Ash is one of the most relevant variables.,8751
Wine_decision_tree.png,Variable Alcalinity of ash is one of the most relevant variables.,8752
Wine_decision_tree.png,Variable Total phenols is one of the most relevant variables.,8753
Wine_decision_tree.png,Variable Flavanoids is one of the most relevant variables.,8754
Wine_decision_tree.png,Variable Nonflavanoid phenols is one of the most relevant variables.,8755
Wine_decision_tree.png,Variable Proanthocyanins is one of the most relevant variables.,8756
Wine_decision_tree.png,Variable Color intensity is one of the most relevant variables.,8757
Wine_decision_tree.png,Variable Hue is one of the most relevant variables.,8758
Wine_decision_tree.png,Variable OD280-OD315 of diluted wines is one of the most relevant variables.,8759
Wine_decision_tree.png,It is possible to state that Class is the first most discriminative variable regarding the class.,8760
Wine_decision_tree.png,It is possible to state that Alcohol is the first most discriminative variable regarding the class.,8761
Wine_decision_tree.png,It is possible to state that Malic acid is the first most discriminative variable regarding the class.,8762
Wine_decision_tree.png,It is possible to state that Ash is the first most discriminative variable regarding the class.,8763
Wine_decision_tree.png,It is possible to state that Alcalinity of ash is the first most discriminative variable regarding the class.,8764
Wine_decision_tree.png,It is possible to state that Total phenols is the first most discriminative variable regarding the class.,8765
Wine_decision_tree.png,It is possible to state that Flavanoids is the first most discriminative variable regarding the class.,8766
Wine_decision_tree.png,It is possible to state that Nonflavanoid phenols is the first most discriminative variable regarding the class.,8767
Wine_decision_tree.png,It is possible to state that Proanthocyanins is the first most discriminative variable regarding the class.,8768
Wine_decision_tree.png,It is possible to state that Color intensity is the first most discriminative variable regarding the class.,8769
Wine_decision_tree.png,It is possible to state that Hue is the first most discriminative variable regarding the class.,8770
Wine_decision_tree.png,It is possible to state that OD280-OD315 of diluted wines is the first most discriminative variable regarding the class.,8771
Wine_decision_tree.png,It is possible to state that Class is the second most discriminative variable regarding the class.,8772
Wine_decision_tree.png,It is possible to state that Alcohol is the second most discriminative variable regarding the class.,8773
Wine_decision_tree.png,It is possible to state that Malic acid is the second most discriminative variable regarding the class.,8774
Wine_decision_tree.png,It is possible to state that Ash is the second most discriminative variable regarding the class.,8775
Wine_decision_tree.png,It is possible to state that Alcalinity of ash is the second most discriminative variable regarding the class.,8776
Wine_decision_tree.png,It is possible to state that Total phenols is the second most discriminative variable regarding the class.,8777
Wine_decision_tree.png,It is possible to state that Flavanoids is the second most discriminative variable regarding the class.,8778
Wine_decision_tree.png,It is possible to state that Nonflavanoid phenols is the second most discriminative variable regarding the class.,8779
Wine_decision_tree.png,It is possible to state that Proanthocyanins is the second most discriminative variable regarding the class.,8780
Wine_decision_tree.png,It is possible to state that Color intensity is the second most discriminative variable regarding the class.,8781
Wine_decision_tree.png,It is possible to state that Hue is the second most discriminative variable regarding the class.,8782
Wine_decision_tree.png,It is possible to state that OD280-OD315 of diluted wines is the second most discriminative variable regarding the class.,8783
Wine_decision_tree.png,"The variable Class discriminates between the target values, as shown in the decision tree.",8784
Wine_decision_tree.png,"The variable Alcohol discriminates between the target values, as shown in the decision tree.",8785
Wine_decision_tree.png,"The variable Malic acid discriminates between the target values, as shown in the decision tree.",8786
Wine_decision_tree.png,"The variable Ash discriminates between the target values, as shown in the decision tree.",8787
Wine_decision_tree.png,"The variable Alcalinity of ash discriminates between the target values, as shown in the decision tree.",8788
Wine_decision_tree.png,"The variable Total phenols discriminates between the target values, as shown in the decision tree.",8789
Wine_decision_tree.png,"The variable Flavanoids discriminates between the target values, as shown in the decision tree.",8790
Wine_decision_tree.png,"The variable Nonflavanoid phenols discriminates between the target values, as shown in the decision tree.",8791
Wine_decision_tree.png,"The variable Proanthocyanins discriminates between the target values, as shown in the decision tree.",8792
Wine_decision_tree.png,"The variable Color intensity discriminates between the target values, as shown in the decision tree.",8793
Wine_decision_tree.png,"The variable Hue discriminates between the target values, as shown in the decision tree.",8794
Wine_decision_tree.png,"The variable OD280-OD315 of diluted wines discriminates between the target values, as shown in the decision tree.",8795
Wine_decision_tree.png,The variable Class seems to be one of the two most relevant features.,8796
Wine_decision_tree.png,The variable Alcohol seems to be one of the two most relevant features.,8797
Wine_decision_tree.png,The variable Malic acid seems to be one of the two most relevant features.,8798
Wine_decision_tree.png,The variable Ash seems to be one of the two most relevant features.,8799
Wine_decision_tree.png,The variable Alcalinity of ash seems to be one of the two most relevant features.,8800
Wine_decision_tree.png,The variable Total phenols seems to be one of the two most relevant features.,8801
Wine_decision_tree.png,The variable Flavanoids seems to be one of the two most relevant features.,8802
Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the two most relevant features.,8803
Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the two most relevant features.,8804
Wine_decision_tree.png,The variable Color intensity seems to be one of the two most relevant features.,8805
Wine_decision_tree.png,The variable Hue seems to be one of the two most relevant features.,8806
Wine_decision_tree.png,The variable OD280-OD315 of diluted wines seems to be one of the two most relevant features.,8807
Wine_decision_tree.png,The variable Class seems to be one of the three most relevant features.,8808
Wine_decision_tree.png,The variable Alcohol seems to be one of the three most relevant features.,8809
Wine_decision_tree.png,The variable Malic acid seems to be one of the three most relevant features.,8810
Wine_decision_tree.png,The variable Ash seems to be one of the three most relevant features.,8811
Wine_decision_tree.png,The variable Alcalinity of ash seems to be one of the three most relevant features.,8812
Wine_decision_tree.png,The variable Total phenols seems to be one of the three most relevant features.,8813
Wine_decision_tree.png,The variable Flavanoids seems to be one of the three most relevant features.,8814
Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the three most relevant features.,8815
Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the three most relevant features.,8816
Wine_decision_tree.png,The variable Color intensity seems to be one of the three most relevant features.,8817
Wine_decision_tree.png,The variable Hue seems to be one of the three most relevant features.,8818
Wine_decision_tree.png,The variable OD280-OD315 of diluted wines seems to be one of the three most relevant features.,8819
Wine_decision_tree.png,The variable Class seems to be one of the four most relevant features.,8820
Wine_decision_tree.png,The variable Alcohol seems to be one of the four most relevant features.,8821
Wine_decision_tree.png,The variable Malic acid seems to be one of the four most relevant features.,8822
Wine_decision_tree.png,The variable Ash seems to be one of the four most relevant features.,8823
Wine_decision_tree.png,The variable Alcalinity of ash seems to be one of the four most relevant features.,8824
Wine_decision_tree.png,The variable Total phenols seems to be one of the four most relevant features.,8825
Wine_decision_tree.png,The variable Flavanoids seems to be one of the four most relevant features.,8826
Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the four most relevant features.,8827
Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the four most relevant features.,8828
Wine_decision_tree.png,The variable Color intensity seems to be one of the four most relevant features.,8829
Wine_decision_tree.png,The variable Hue seems to be one of the four most relevant features.,8830
Wine_decision_tree.png,The variable OD280-OD315 of diluted wines seems to be one of the four most relevant features.,8831
Wine_decision_tree.png,The variable Class seems to be one of the five most relevant features.,8832
Wine_decision_tree.png,The variable Alcohol seems to be one of the five most relevant features.,8833
Wine_decision_tree.png,The variable Malic acid seems to be one of the five most relevant features.,8834
Wine_decision_tree.png,The variable Ash seems to be one of the five most relevant features.,8835
Wine_decision_tree.png,The variable Alcalinity of ash seems to be one of the five most relevant features.,8836
Wine_decision_tree.png,The variable Total phenols seems to be one of the five most relevant features.,8837
Wine_decision_tree.png,The variable Flavanoids seems to be one of the five most relevant features.,8838
Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the five most relevant features.,8839
Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the five most relevant features.,8840
Wine_decision_tree.png,The variable Color intensity seems to be one of the five most relevant features.,8841
Wine_decision_tree.png,The variable Hue seems to be one of the five most relevant features.,8842
Wine_decision_tree.png,The variable OD280-OD315 of diluted wines seems to be one of the five most relevant features.,8843
Wine_decision_tree.png,It is clear that variable Class is one of the two most relevant features.,8844
Wine_decision_tree.png,It is clear that variable Alcohol is one of the two most relevant features.,8845
Wine_decision_tree.png,It is clear that variable Malic acid is one of the two most relevant features.,8846
Wine_decision_tree.png,It is clear that variable Ash is one of the two most relevant features.,8847
Wine_decision_tree.png,It is clear that variable Alcalinity of ash is one of the two most relevant features.,8848
Wine_decision_tree.png,It is clear that variable Total phenols is one of the two most relevant features.,8849
Wine_decision_tree.png,It is clear that variable Flavanoids is one of the two most relevant features.,8850
Wine_decision_tree.png,It is clear that variable Nonflavanoid phenols is one of the two most relevant features.,8851
Wine_decision_tree.png,It is clear that variable Proanthocyanins is one of the two most relevant features.,8852
Wine_decision_tree.png,It is clear that variable Color intensity is one of the two most relevant features.,8853
Wine_decision_tree.png,It is clear that variable Hue is one of the two most relevant features.,8854
Wine_decision_tree.png,It is clear that variable OD280-OD315 of diluted wines is one of the two most relevant features.,8855
Wine_decision_tree.png,It is clear that variable Class is one of the three most relevant features.,8856
Wine_decision_tree.png,It is clear that variable Alcohol is one of the three most relevant features.,8857
Wine_decision_tree.png,It is clear that variable Malic acid is one of the three most relevant features.,8858
Wine_decision_tree.png,It is clear that variable Ash is one of the three most relevant features.,8859
Wine_decision_tree.png,It is clear that variable Alcalinity of ash is one of the three most relevant features.,8860
Wine_decision_tree.png,It is clear that variable Total phenols is one of the three most relevant features.,8861
Wine_decision_tree.png,It is clear that variable Flavanoids is one of the three most relevant features.,8862
Wine_decision_tree.png,It is clear that variable Nonflavanoid phenols is one of the three most relevant features.,8863
Wine_decision_tree.png,It is clear that variable Proanthocyanins is one of the three most relevant features.,8864
Wine_decision_tree.png,It is clear that variable Color intensity is one of the three most relevant features.,8865
Wine_decision_tree.png,It is clear that variable Hue is one of the three most relevant features.,8866
Wine_decision_tree.png,It is clear that variable OD280-OD315 of diluted wines is one of the three most relevant features.,8867
Wine_decision_tree.png,It is clear that variable Class is one of the four most relevant features.,8868
Wine_decision_tree.png,It is clear that variable Alcohol is one of the four most relevant features.,8869
Wine_decision_tree.png,It is clear that variable Malic acid is one of the four most relevant features.,8870
Wine_decision_tree.png,It is clear that variable Ash is one of the four most relevant features.,8871
Wine_decision_tree.png,It is clear that variable Alcalinity of ash is one of the four most relevant features.,8872
Wine_decision_tree.png,It is clear that variable Total phenols is one of the four most relevant features.,8873
Wine_decision_tree.png,It is clear that variable Flavanoids is one of the four most relevant features.,8874
Wine_decision_tree.png,It is clear that variable Nonflavanoid phenols is one of the four most relevant features.,8875
Wine_decision_tree.png,It is clear that variable Proanthocyanins is one of the four most relevant features.,8876
Wine_decision_tree.png,It is clear that variable Color intensity is one of the four most relevant features.,8877
Wine_decision_tree.png,It is clear that variable Hue is one of the four most relevant features.,8878
Wine_decision_tree.png,It is clear that variable OD280-OD315 of diluted wines is one of the four most relevant features.,8879
Wine_decision_tree.png,It is clear that variable Class is one of the five most relevant features.,8880
Wine_decision_tree.png,It is clear that variable Alcohol is one of the five most relevant features.,8881
Wine_decision_tree.png,It is clear that variable Malic acid is one of the five most relevant features.,8882
Wine_decision_tree.png,It is clear that variable Ash is one of the five most relevant features.,8883
Wine_decision_tree.png,It is clear that variable Alcalinity of ash is one of the five most relevant features.,8884
Wine_decision_tree.png,It is clear that variable Total phenols is one of the five most relevant features.,8885
Wine_decision_tree.png,It is clear that variable Flavanoids is one of the five most relevant features.,8886
Wine_decision_tree.png,It is clear that variable Nonflavanoid phenols is one of the five most relevant features.,8887
Wine_decision_tree.png,It is clear that variable Proanthocyanins is one of the five most relevant features.,8888
Wine_decision_tree.png,It is clear that variable Color intensity is one of the five most relevant features.,8889
Wine_decision_tree.png,It is clear that variable Hue is one of the five most relevant features.,8890
Wine_decision_tree.png,It is clear that variable OD280-OD315 of diluted wines is one of the five most relevant features.,8891
Wine_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",8892
Wine_correlation_heatmap.png,Variables Alcohol and Class are redundant.,8893
Wine_correlation_heatmap.png,Variables Malic acid and Class are redundant.,8894
Wine_correlation_heatmap.png,Variables Ash and Class are redundant.,8895
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Class are redundant.,8896
Wine_correlation_heatmap.png,Variables Total phenols and Class are redundant.,8897
Wine_correlation_heatmap.png,Variables Flavanoids and Class are redundant.,8898
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Class are redundant.,8899
Wine_correlation_heatmap.png,Variables Proanthocyanins and Class are redundant.,8900
Wine_correlation_heatmap.png,Variables Color intensity and Class are redundant.,8901
Wine_correlation_heatmap.png,Variables Hue and Class are redundant.,8902
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Class are redundant.,8903
Wine_correlation_heatmap.png,Variables Class and Alcohol are redundant.,8904
Wine_correlation_heatmap.png,Variables Malic acid and Alcohol are redundant.,8905
Wine_correlation_heatmap.png,Variables Ash and Alcohol are redundant.,8906
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Alcohol are redundant.,8907
Wine_correlation_heatmap.png,Variables Total phenols and Alcohol are redundant.,8908
Wine_correlation_heatmap.png,Variables Flavanoids and Alcohol are redundant.,8909
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Alcohol are redundant.,8910
Wine_correlation_heatmap.png,Variables Proanthocyanins and Alcohol are redundant.,8911
Wine_correlation_heatmap.png,Variables Color intensity and Alcohol are redundant.,8912
Wine_correlation_heatmap.png,Variables Hue and Alcohol are redundant.,8913
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Alcohol are redundant.,8914
Wine_correlation_heatmap.png,Variables Class and Malic acid are redundant.,8915
Wine_correlation_heatmap.png,Variables Alcohol and Malic acid are redundant.,8916
Wine_correlation_heatmap.png,Variables Ash and Malic acid are redundant.,8917
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Malic acid are redundant.,8918
Wine_correlation_heatmap.png,Variables Total phenols and Malic acid are redundant.,8919
Wine_correlation_heatmap.png,Variables Flavanoids and Malic acid are redundant.,8920
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Malic acid are redundant.,8921
Wine_correlation_heatmap.png,Variables Proanthocyanins and Malic acid are redundant.,8922
Wine_correlation_heatmap.png,Variables Color intensity and Malic acid are redundant.,8923
Wine_correlation_heatmap.png,Variables Hue and Malic acid are redundant.,8924
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Malic acid are redundant.,8925
Wine_correlation_heatmap.png,Variables Class and Ash are redundant.,8926
Wine_correlation_heatmap.png,Variables Alcohol and Ash are redundant.,8927
Wine_correlation_heatmap.png,Variables Malic acid and Ash are redundant.,8928
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Ash are redundant.,8929
Wine_correlation_heatmap.png,Variables Total phenols and Ash are redundant.,8930
Wine_correlation_heatmap.png,Variables Flavanoids and Ash are redundant.,8931
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Ash are redundant.,8932
Wine_correlation_heatmap.png,Variables Proanthocyanins and Ash are redundant.,8933
Wine_correlation_heatmap.png,Variables Color intensity and Ash are redundant.,8934
Wine_correlation_heatmap.png,Variables Hue and Ash are redundant.,8935
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Ash are redundant.,8936
Wine_correlation_heatmap.png,Variables Class and Alcalinity of ash are redundant.,8937
Wine_correlation_heatmap.png,Variables Alcohol and Alcalinity of ash are redundant.,8938
Wine_correlation_heatmap.png,Variables Malic acid and Alcalinity of ash are redundant.,8939
Wine_correlation_heatmap.png,Variables Ash and Alcalinity of ash are redundant.,8940
Wine_correlation_heatmap.png,Variables Total phenols and Alcalinity of ash are redundant.,8941
Wine_correlation_heatmap.png,Variables Flavanoids and Alcalinity of ash are redundant.,8942
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Alcalinity of ash are redundant.,8943
Wine_correlation_heatmap.png,Variables Proanthocyanins and Alcalinity of ash are redundant.,8944
Wine_correlation_heatmap.png,Variables Color intensity and Alcalinity of ash are redundant.,8945
Wine_correlation_heatmap.png,Variables Hue and Alcalinity of ash are redundant.,8946
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Alcalinity of ash are redundant.,8947
Wine_correlation_heatmap.png,Variables Class and Total phenols are redundant.,8948
Wine_correlation_heatmap.png,Variables Alcohol and Total phenols are redundant.,8949
Wine_correlation_heatmap.png,Variables Malic acid and Total phenols are redundant.,8950
Wine_correlation_heatmap.png,Variables Ash and Total phenols are redundant.,8951
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Total phenols are redundant.,8952
Wine_correlation_heatmap.png,Variables Flavanoids and Total phenols are redundant.,8953
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Total phenols are redundant.,8954
Wine_correlation_heatmap.png,Variables Proanthocyanins and Total phenols are redundant.,8955
Wine_correlation_heatmap.png,Variables Color intensity and Total phenols are redundant.,8956
Wine_correlation_heatmap.png,Variables Hue and Total phenols are redundant.,8957
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Total phenols are redundant.,8958
Wine_correlation_heatmap.png,Variables Class and Flavanoids are redundant.,8959
Wine_correlation_heatmap.png,Variables Alcohol and Flavanoids are redundant.,8960
Wine_correlation_heatmap.png,Variables Malic acid and Flavanoids are redundant.,8961
Wine_correlation_heatmap.png,Variables Ash and Flavanoids are redundant.,8962
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Flavanoids are redundant.,8963
Wine_correlation_heatmap.png,Variables Total phenols and Flavanoids are redundant.,8964
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Flavanoids are redundant.,8965
Wine_correlation_heatmap.png,Variables Proanthocyanins and Flavanoids are redundant.,8966
Wine_correlation_heatmap.png,Variables Color intensity and Flavanoids are redundant.,8967
Wine_correlation_heatmap.png,Variables Hue and Flavanoids are redundant.,8968
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Flavanoids are redundant.,8969
Wine_correlation_heatmap.png,Variables Class and Nonflavanoid phenols are redundant.,8970
Wine_correlation_heatmap.png,Variables Alcohol and Nonflavanoid phenols are redundant.,8971
Wine_correlation_heatmap.png,Variables Malic acid and Nonflavanoid phenols are redundant.,8972
Wine_correlation_heatmap.png,Variables Ash and Nonflavanoid phenols are redundant.,8973
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Nonflavanoid phenols are redundant.,8974
Wine_correlation_heatmap.png,Variables Total phenols and Nonflavanoid phenols are redundant.,8975
Wine_correlation_heatmap.png,Variables Flavanoids and Nonflavanoid phenols are redundant.,8976
Wine_correlation_heatmap.png,Variables Proanthocyanins and Nonflavanoid phenols are redundant.,8977
Wine_correlation_heatmap.png,Variables Color intensity and Nonflavanoid phenols are redundant.,8978
Wine_correlation_heatmap.png,Variables Hue and Nonflavanoid phenols are redundant.,8979
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Nonflavanoid phenols are redundant.,8980
Wine_correlation_heatmap.png,Variables Class and Proanthocyanins are redundant.,8981
Wine_correlation_heatmap.png,Variables Alcohol and Proanthocyanins are redundant.,8982
Wine_correlation_heatmap.png,Variables Malic acid and Proanthocyanins are redundant.,8983
Wine_correlation_heatmap.png,Variables Ash and Proanthocyanins are redundant.,8984
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Proanthocyanins are redundant.,8985
Wine_correlation_heatmap.png,Variables Total phenols and Proanthocyanins are redundant.,8986
Wine_correlation_heatmap.png,Variables Flavanoids and Proanthocyanins are redundant.,8987
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Proanthocyanins are redundant.,8988
Wine_correlation_heatmap.png,Variables Color intensity and Proanthocyanins are redundant.,8989
Wine_correlation_heatmap.png,Variables Hue and Proanthocyanins are redundant.,8990
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Proanthocyanins are redundant.,8991
Wine_correlation_heatmap.png,Variables Class and Color intensity are redundant.,8992
Wine_correlation_heatmap.png,Variables Alcohol and Color intensity are redundant.,8993
Wine_correlation_heatmap.png,Variables Malic acid and Color intensity are redundant.,8994
Wine_correlation_heatmap.png,Variables Ash and Color intensity are redundant.,8995
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Color intensity are redundant.,8996
Wine_correlation_heatmap.png,Variables Total phenols and Color intensity are redundant.,8997
Wine_correlation_heatmap.png,Variables Flavanoids and Color intensity are redundant.,8998
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Color intensity are redundant.,8999
Wine_correlation_heatmap.png,Variables Proanthocyanins and Color intensity are redundant.,9000
Wine_correlation_heatmap.png,Variables Hue and Color intensity are redundant.,9001
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Color intensity are redundant.,9002
Wine_correlation_heatmap.png,Variables Class and Hue are redundant.,9003
Wine_correlation_heatmap.png,Variables Alcohol and Hue are redundant.,9004
Wine_correlation_heatmap.png,Variables Malic acid and Hue are redundant.,9005
Wine_correlation_heatmap.png,Variables Ash and Hue are redundant.,9006
Wine_correlation_heatmap.png,Variables Alcalinity of ash and Hue are redundant.,9007
Wine_correlation_heatmap.png,Variables Total phenols and Hue are redundant.,9008
Wine_correlation_heatmap.png,Variables Flavanoids and Hue are redundant.,9009
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Hue are redundant.,9010
Wine_correlation_heatmap.png,Variables Proanthocyanins and Hue are redundant.,9011
Wine_correlation_heatmap.png,Variables Color intensity and Hue are redundant.,9012
Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Hue are redundant.,9013
Wine_correlation_heatmap.png,Variables Class and OD280-OD315 of diluted wines are redundant.,9014
Wine_correlation_heatmap.png,Variables Alcohol and OD280-OD315 of diluted wines are redundant.,9015
Wine_correlation_heatmap.png,Variables Malic acid and OD280-OD315 of diluted wines are redundant.,9016
Wine_correlation_heatmap.png,Variables Ash and OD280-OD315 of diluted wines are redundant.,9017
Wine_correlation_heatmap.png,Variables Alcalinity of ash and OD280-OD315 of diluted wines are redundant.,9018
Wine_correlation_heatmap.png,Variables Total phenols and OD280-OD315 of diluted wines are redundant.,9019
Wine_correlation_heatmap.png,Variables Flavanoids and OD280-OD315 of diluted wines are redundant.,9020
Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant.,9021
Wine_correlation_heatmap.png,Variables Proanthocyanins and OD280-OD315 of diluted wines are redundant.,9022
Wine_correlation_heatmap.png,Variables Color intensity and OD280-OD315 of diluted wines are redundant.,9023
Wine_correlation_heatmap.png,Variables Hue and OD280-OD315 of diluted wines are redundant.,9024
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Color intensity are redundant, but we can’t say the same for the pair Alcohol and Flavanoids.",9025
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Hue are redundant, but we can’t say the same for the pair Total phenols and OD280-OD315 of diluted wines.",9026
Wine_correlation_heatmap.png,"Variables Alcohol and Ash are redundant, but we can’t say the same for the pair Alcalinity of ash and Nonflavanoid phenols.",9027
Wine_correlation_heatmap.png,"Variables Total phenols and Color intensity are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9028
Wine_correlation_heatmap.png,"Variables Ash and Flavanoids are redundant, but we can’t say the same for the pair Total phenols and Color intensity.",9029
Wine_correlation_heatmap.png,"Variables Ash and Hue are redundant, but we can’t say the same for the pair Malic acid and Proanthocyanins.",9030
Wine_correlation_heatmap.png,"Variables Malic acid and Flavanoids are redundant, but we can’t say the same for the pair Alcalinity of ash and OD280-OD315 of diluted wines.",9031
Wine_correlation_heatmap.png,"Variables Ash and Total phenols are redundant, but we can’t say the same for the pair Hue and OD280-OD315 of diluted wines.",9032
Wine_correlation_heatmap.png,"Variables Malic acid and Hue are redundant, but we can’t say the same for the pair Ash and Nonflavanoid phenols.",9033
Wine_correlation_heatmap.png,"Variables Ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Flavanoids and Hue.",9034
Wine_correlation_heatmap.png,"Variables Malic acid and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcalinity of ash and Hue.",9035
Wine_correlation_heatmap.png,"Variables Ash and Hue are redundant, but we can’t say the same for the pair Alcohol and Malic acid.",9036
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Color intensity are redundant, but we can’t say the same for the pair Ash and Alcalinity of ash.",9037
Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Alcohol and Alcalinity of ash.",9038
Wine_correlation_heatmap.png,"Variables Total phenols and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Ash and OD280-OD315 of diluted wines.",9039
Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Flavanoids and Color intensity.",9040
Wine_correlation_heatmap.png,"Variables Malic acid and Alcalinity of ash are redundant, but we can’t say the same for the pair Nonflavanoid phenols and OD280-OD315 of diluted wines.",9041
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Proanthocyanins are redundant, but we can’t say the same for the pair Alcohol and OD280-OD315 of diluted wines.",9042
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Proanthocyanins are redundant, but we can’t say the same for the pair Malic acid and Ash.",9043
Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Ash and Alcalinity of ash.",9044
Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Total phenols and Color intensity.",9045
Wine_correlation_heatmap.png,"Variables Malic acid and Color intensity are redundant, but we can’t say the same for the pair Ash and Nonflavanoid phenols.",9046
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Color intensity are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9047
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Hue are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Color intensity.",9048
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Total phenols are redundant, but we can’t say the same for the pair Alcohol and Malic acid.",9049
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Hue are redundant, but we can’t say the same for the pair Ash and Flavanoids.",9050
Wine_correlation_heatmap.png,"Variables Ash and Color intensity are redundant, but we can’t say the same for the pair Malic acid and OD280-OD315 of diluted wines.",9051
Wine_correlation_heatmap.png,"Variables Ash and Alcalinity of ash are redundant, but we can’t say the same for the pair Malic acid and Color intensity.",9052
Wine_correlation_heatmap.png,"Variables Total phenols and Flavanoids are redundant, but we can’t say the same for the pair Ash and OD280-OD315 of diluted wines.",9053
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Hue are redundant, but we can’t say the same for the pair Alcohol and OD280-OD315 of diluted wines.",9054
Wine_correlation_heatmap.png,"Variables Malic acid and Flavanoids are redundant, but we can’t say the same for the pair Alcohol and Nonflavanoid phenols.",9055
Wine_correlation_heatmap.png,"Variables Flavanoids and Proanthocyanins are redundant, but we can’t say the same for the pair Nonflavanoid phenols and OD280-OD315 of diluted wines.",9056
Wine_correlation_heatmap.png,"Variables Malic acid and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Alcohol and Alcalinity of ash.",9057
Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Ash and OD280-OD315 of diluted wines.",9058
Wine_correlation_heatmap.png,"Variables Alcohol and Proanthocyanins are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Color intensity.",9059
Wine_correlation_heatmap.png,"Variables Malic acid and Alcalinity of ash are redundant, but we can’t say the same for the pair Total phenols and Flavanoids.",9060
Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Malic acid and Nonflavanoid phenols.",9061
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9062
Wine_correlation_heatmap.png,"Variables Alcohol and Hue are redundant, but we can’t say the same for the pair Ash and Color intensity.",9063
Wine_correlation_heatmap.png,"Variables Ash and Color intensity are redundant, but we can’t say the same for the pair Total phenols and Nonflavanoid phenols.",9064
Wine_correlation_heatmap.png,"Variables Hue and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Malic acid and Nonflavanoid phenols.",9065
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Color intensity are redundant, but we can’t say the same for the pair Total phenols and Proanthocyanins.",9066
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Flavanoids are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9067
Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Ash and Flavanoids.",9068
Wine_correlation_heatmap.png,"Variables Flavanoids and Color intensity are redundant, but we can’t say the same for the pair Ash and OD280-OD315 of diluted wines.",9069
Wine_correlation_heatmap.png,"Variables Malic acid and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Proanthocyanins.",9070
Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Total phenols and Flavanoids.",9071
Wine_correlation_heatmap.png,"Variables Malic acid and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and Color intensity.",9072
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Proanthocyanins are redundant, but we can’t say the same for the pair Alcohol and Ash.",9073
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Flavanoids are redundant, but we can’t say the same for the pair Malic acid and OD280-OD315 of diluted wines.",9074
Wine_correlation_heatmap.png,"Variables Alcohol and Ash are redundant, but we can’t say the same for the pair Total phenols and OD280-OD315 of diluted wines.",9075
Wine_correlation_heatmap.png,"Variables Flavanoids and Proanthocyanins are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Hue.",9076
Wine_correlation_heatmap.png,"Variables Alcohol and Flavanoids are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9077
Wine_correlation_heatmap.png,"Variables Total phenols and Color intensity are redundant, but we can’t say the same for the pair Hue and OD280-OD315 of diluted wines.",9078
Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Color intensity and OD280-OD315 of diluted wines.",9079
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Hue are redundant, but we can’t say the same for the pair Proanthocyanins and Color intensity.",9080
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Flavanoids are redundant, but we can’t say the same for the pair Total phenols and Nonflavanoid phenols.",9081
Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Alcohol and Total phenols.",9082
Wine_correlation_heatmap.png,"Variables Ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Flavanoids and Hue.",9083
Wine_correlation_heatmap.png,"Variables Total phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Malic acid and Flavanoids.",9084
Wine_correlation_heatmap.png,"Variables Total phenols and Flavanoids are redundant, but we can’t say the same for the pair Alcohol and OD280-OD315 of diluted wines.",9085
Wine_correlation_heatmap.png,"Variables Flavanoids and Color intensity are redundant, but we can’t say the same for the pair Malic acid and OD280-OD315 of diluted wines.",9086
Wine_correlation_heatmap.png,"Variables Malic acid and Hue are redundant, but we can’t say the same for the pair Alcohol and Nonflavanoid phenols.",9087
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcohol and Ash.",9088
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Color intensity are redundant, but we can’t say the same for the pair Proanthocyanins and OD280-OD315 of diluted wines.",9089
Wine_correlation_heatmap.png,"Variables Alcohol and Malic acid are redundant, but we can’t say the same for the pair Alcalinity of ash and OD280-OD315 of diluted wines.",9090
Wine_correlation_heatmap.png,"Variables Alcohol and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9091
Wine_correlation_heatmap.png,"Variables Alcohol and Flavanoids are redundant, but we can’t say the same for the pair Malic acid and Nonflavanoid phenols.",9092
Wine_correlation_heatmap.png,"Variables Malic acid and Color intensity are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Hue.",9093
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcohol and Hue.",9094
Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Alcalinity of ash and Flavanoids.",9095
Wine_correlation_heatmap.png,"Variables Alcohol and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcalinity of ash and Hue.",9096
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Flavanoids are redundant, but we can’t say the same for the pair Ash and Proanthocyanins.",9097
Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Nonflavanoid phenols and OD280-OD315 of diluted wines.",9098
Wine_correlation_heatmap.png,"Variables Malic acid and Color intensity are redundant, but we can’t say the same for the pair Alcalinity of ash and OD280-OD315 of diluted wines.",9099
Wine_correlation_heatmap.png,"Variables Total phenols and Flavanoids are redundant, but we can’t say the same for the pair Alcohol and Malic acid.",9100
Wine_correlation_heatmap.png,"Variables Flavanoids and Proanthocyanins are redundant, but we can’t say the same for the pair Ash and Hue.",9101
Wine_correlation_heatmap.png,"Variables Alcohol and Color intensity are redundant, but we can’t say the same for the pair Total phenols and Nonflavanoid phenols.",9102
Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Total phenols and Hue.",9103
Wine_correlation_heatmap.png,"Variables Color intensity and Hue are redundant, but we can’t say the same for the pair Malic acid and Alcalinity of ash.",9104
Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Alcalinity of ash and Color intensity.",9105
Wine_correlation_heatmap.png,"Variables Ash and Proanthocyanins are redundant, but we can’t say the same for the pair Malic acid and Total phenols.",9106
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Proanthocyanins are redundant, but we can’t say the same for the pair Malic acid and Ash.",9107
Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Ash and Proanthocyanins.",9108
Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Malic acid and Ash.",9109
Wine_correlation_heatmap.png,"Variables Flavanoids and Proanthocyanins are redundant, but we can’t say the same for the pair Alcalinity of ash and OD280-OD315 of diluted wines.",9110
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Flavanoids and Color intensity.",9111
Wine_correlation_heatmap.png,"Variables Flavanoids and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and Color intensity.",9112
Wine_correlation_heatmap.png,"Variables Alcohol and Hue are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9113
Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Malic acid and Color intensity.",9114
Wine_correlation_heatmap.png,"Variables Malic acid and Color intensity are redundant, but we can’t say the same for the pair Total phenols and OD280-OD315 of diluted wines.",9115
Wine_correlation_heatmap.png,"Variables Malic acid and Alcalinity of ash are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9116
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Total phenols are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9117
Wine_correlation_heatmap.png,"Variables Total phenols and Flavanoids are redundant, but we can’t say the same for the pair Proanthocyanins and Color intensity.",9118
Wine_correlation_heatmap.png,"Variables Alcohol and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Total phenols and Nonflavanoid phenols.",9119
Wine_correlation_heatmap.png,"Variables Alcohol and Color intensity are redundant, but we can’t say the same for the pair Alcalinity of ash and Hue.",9120
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Hue are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9121
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Hue are redundant, but we can’t say the same for the pair Total phenols and Color intensity.",9122
Wine_correlation_heatmap.png,"Variables Ash and Proanthocyanins are redundant, but we can’t say the same for the pair Total phenols and Flavanoids.",9123
Wine_correlation_heatmap.png,"Variables Color intensity and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcalinity of ash and Proanthocyanins.",9124
Wine_correlation_heatmap.png,"Variables Alcohol and Ash are redundant, but we can’t say the same for the pair Malic acid and Proanthocyanins.",9125
Wine_correlation_heatmap.png,"Variables Ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9126
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Hue are redundant, but we can’t say the same for the pair Flavanoids and OD280-OD315 of diluted wines.",9127
Wine_correlation_heatmap.png,"Variables Ash and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and Proanthocyanins.",9128
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Hue are redundant, but we can’t say the same for the pair Malic acid and OD280-OD315 of diluted wines.",9129
Wine_correlation_heatmap.png,"Variables Color intensity and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Ash and Nonflavanoid phenols.",9130
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Color intensity are redundant, but we can’t say the same for the pair Flavanoids and Nonflavanoid phenols.",9131
Wine_correlation_heatmap.png,"Variables Ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Malic acid and Hue.",9132
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Proanthocyanins are redundant, but we can’t say the same for the pair Alcohol and Color intensity.",9133
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Color intensity are redundant, but we can’t say the same for the pair Total phenols and Nonflavanoid phenols.",9134
Wine_correlation_heatmap.png,"Variables Ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Proanthocyanins and Color intensity.",9135
Wine_correlation_heatmap.png,"Variables Total phenols and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Malic acid and Color intensity.",9136
Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Flavanoids and OD280-OD315 of diluted wines.",9137
Wine_correlation_heatmap.png,"Variables Ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Total phenols and Proanthocyanins.",9138
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Hue are redundant, but we can’t say the same for the pair Ash and OD280-OD315 of diluted wines.",9139
Wine_correlation_heatmap.png,"Variables Malic acid and Total phenols are redundant, but we can’t say the same for the pair Alcohol and OD280-OD315 of diluted wines.",9140
Wine_correlation_heatmap.png,"Variables Malic acid and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and OD280-OD315 of diluted wines.",9141
Wine_correlation_heatmap.png,"Variables Flavanoids and Color intensity are redundant, but we can’t say the same for the pair Alcalinity of ash and Proanthocyanins.",9142
Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9143
Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Alcohol and Total phenols.",9144
Wine_correlation_heatmap.png,"Variables Malic acid and Flavanoids are redundant, but we can’t say the same for the pair Alcohol and Proanthocyanins.",9145
Wine_correlation_heatmap.png,"Variables Alcohol and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Proanthocyanins and Color intensity.",9146
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Color intensity are redundant, but we can’t say the same for the pair Flavanoids and OD280-OD315 of diluted wines.",9147
Wine_correlation_heatmap.png,"Variables Alcohol and Proanthocyanins are redundant, but we can’t say the same for the pair Alcalinity of ash and Color intensity.",9148
Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Ash and Total phenols.",9149
Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9150
Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Total phenols and Nonflavanoid phenols.",9151
Wine_correlation_heatmap.png,"Variables Ash and Hue are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9152
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Color intensity and Hue.",9153
Wine_correlation_heatmap.png,"Variables Ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcohol and Proanthocyanins.",9154
Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Color intensity.",9155
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Color intensity are redundant, but we can’t say the same for the pair Malic acid and Flavanoids.",9156
Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Flavanoids and Color intensity.",9157
Wine_correlation_heatmap.png,"Variables Alcohol and Color intensity are redundant, but we can’t say the same for the pair Nonflavanoid phenols and OD280-OD315 of diluted wines.",9158
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Hue are redundant, but we can’t say the same for the pair Malic acid and Proanthocyanins.",9159
Wine_correlation_heatmap.png,"Variables Ash and Alcalinity of ash are redundant, but we can’t say the same for the pair Total phenols and Nonflavanoid phenols.",9160
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Total phenols are redundant, but we can’t say the same for the pair Malic acid and Proanthocyanins.",9161
Wine_correlation_heatmap.png,"Variables Total phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Hue.",9162
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Hue are redundant, but we can’t say the same for the pair Ash and Nonflavanoid phenols.",9163
Wine_correlation_heatmap.png,"Variables Color intensity and Hue are redundant, but we can’t say the same for the pair Alcohol and Flavanoids.",9164
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Color intensity are redundant, but we can’t say the same for the pair Hue and OD280-OD315 of diluted wines.",9165
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Malic acid and Flavanoids.",9166
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Proanthocyanins are redundant, but we can’t say the same for the pair Alcohol and Color intensity.",9167
Wine_correlation_heatmap.png,"Variables Ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9168
Wine_correlation_heatmap.png,"Variables Hue and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Proanthocyanins.",9169
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Malic acid and Ash.",9170
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Total phenols are redundant, but we can’t say the same for the pair Alcohol and Color intensity.",9171
Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Alcohol and Ash.",9172
Wine_correlation_heatmap.png,"Variables Hue and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcohol and Nonflavanoid phenols.",9173
Wine_correlation_heatmap.png,"Variables Alcohol and Flavanoids are redundant, but we can’t say the same for the pair Ash and Total phenols.",9174
Wine_correlation_heatmap.png,"Variables Alcohol and Color intensity are redundant, but we can’t say the same for the pair Malic acid and OD280-OD315 of diluted wines.",9175
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Hue are redundant, but we can’t say the same for the pair Malic acid and Color intensity.",9176
Wine_correlation_heatmap.png,"Variables Alcohol and Proanthocyanins are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9177
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Alcohol and Proanthocyanins.",9178
Wine_correlation_heatmap.png,"Variables Ash and Color intensity are redundant, but we can’t say the same for the pair Flavanoids and OD280-OD315 of diluted wines.",9179
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Malic acid and Ash.",9180
Wine_correlation_heatmap.png,"Variables Total phenols and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Proanthocyanins.",9181
Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9182
Wine_correlation_heatmap.png,"Variables Malic acid and Flavanoids are redundant, but we can’t say the same for the pair Alcohol and Hue.",9183
Wine_correlation_heatmap.png,"Variables Ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcalinity of ash and Color intensity.",9184
Wine_correlation_heatmap.png,"Variables Color intensity and Hue are redundant, but we can’t say the same for the pair Total phenols and Flavanoids.",9185
Wine_correlation_heatmap.png,"Variables Malic acid and Total phenols are redundant, but we can’t say the same for the pair Alcohol and Color intensity.",9186
Wine_correlation_heatmap.png,"Variables Alcohol and Flavanoids are redundant, but we can’t say the same for the pair Ash and Hue.",9187
Wine_correlation_heatmap.png,"Variables Alcohol and Malic acid are redundant, but we can’t say the same for the pair Total phenols and Flavanoids.",9188
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Hue are redundant, but we can’t say the same for the pair Ash and Color intensity.",9189
Wine_correlation_heatmap.png,"Variables Ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Hue and OD280-OD315 of diluted wines.",9190
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Ash and Nonflavanoid phenols.",9191
Wine_correlation_heatmap.png,"Variables Ash and Alcalinity of ash are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Color intensity.",9192
Wine_correlation_heatmap.png,"Variables Ash and Total phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Flavanoids.",9193
Wine_correlation_heatmap.png,"Variables Alcohol and Ash are redundant, but we can’t say the same for the pair Malic acid and Flavanoids.",9194
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcohol and Hue.",9195
Wine_correlation_heatmap.png,"Variables Color intensity and Hue are redundant, but we can’t say the same for the pair Malic acid and Total phenols.",9196
Wine_correlation_heatmap.png,"Variables Flavanoids and Color intensity are redundant, but we can’t say the same for the pair Ash and Total phenols.",9197
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Flavanoids and Color intensity.",9198
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9199
Wine_correlation_heatmap.png,"Variables Alcohol and Proanthocyanins are redundant, but we can’t say the same for the pair Hue and OD280-OD315 of diluted wines.",9200
Wine_correlation_heatmap.png,"Variables Ash and Proanthocyanins are redundant, but we can’t say the same for the pair Flavanoids and Color intensity.",9201
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Color intensity are redundant, but we can’t say the same for the pair Alcohol and Proanthocyanins.",9202
Wine_correlation_heatmap.png,"Variables Ash and Alcalinity of ash are redundant, but we can’t say the same for the pair Total phenols and Proanthocyanins.",9203
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and OD280-OD315 of diluted wines.",9204
Wine_correlation_heatmap.png,"Variables Ash and Proanthocyanins are redundant, but we can’t say the same for the pair Color intensity and OD280-OD315 of diluted wines.",9205
Wine_correlation_heatmap.png,"Variables Ash and Color intensity are redundant, but we can’t say the same for the pair Total phenols and Flavanoids.",9206
Wine_correlation_heatmap.png,"Variables Ash and Proanthocyanins are redundant, but we can’t say the same for the pair Alcalinity of ash and OD280-OD315 of diluted wines.",9207
Wine_correlation_heatmap.png,"Variables Ash and Total phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Proanthocyanins.",9208
Wine_correlation_heatmap.png,"Variables Alcohol and Flavanoids are redundant, but we can’t say the same for the pair Color intensity and Hue.",9209
Wine_correlation_heatmap.png,"Variables Proanthocyanins and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Total phenols and Hue.",9210
Wine_correlation_heatmap.png,"Variables Total phenols and Flavanoids are redundant, but we can’t say the same for the pair Malic acid and Ash.",9211
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Color intensity are redundant, but we can’t say the same for the pair Malic acid and OD280-OD315 of diluted wines.",9212
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Ash and Flavanoids.",9213
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Total phenols are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9214
Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Color intensity and Hue.",9215
Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Proanthocyanins and OD280-OD315 of diluted wines.",9216
Wine_correlation_heatmap.png,"Variables Ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9217
Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Flavanoids and Nonflavanoid phenols.",9218
Wine_correlation_heatmap.png,"Variables Alcohol and Malic acid are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Color intensity.",9219
Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Malic acid and Hue.",9220
Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9221
Wine_correlation_heatmap.png,"Variables Total phenols and Flavanoids are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9222
Wine_correlation_heatmap.png,"Variables Malic acid and Total phenols are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Hue.",9223
Wine_correlation_heatmap.png,"Variables Malic acid and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Ash and Proanthocyanins.",9224
Wine_correlation_heatmap.png,"Variables Malic acid and Alcalinity of ash are redundant, but we can’t say the same for the pair Color intensity and OD280-OD315 of diluted wines.",9225
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and Proanthocyanins are redundant, but we can’t say the same for the pair Malic acid and Flavanoids.",9226
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcohol and Ash.",9227
Wine_correlation_heatmap.png,"Variables Total phenols and Color intensity are redundant, but we can’t say the same for the pair Ash and Alcalinity of ash.",9228
Wine_correlation_heatmap.png,"Variables Alcohol and Hue are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Proanthocyanins.",9229
Wine_correlation_heatmap.png,"Variables Ash and Color intensity are redundant, but we can’t say the same for the pair Alcohol and OD280-OD315 of diluted wines.",9230
Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Color intensity are redundant, but we can’t say the same for the pair Alcohol and Hue.",9231
Wine_correlation_heatmap.png,"Variables Malic acid and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Proanthocyanins and Color intensity.",9232
Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Total phenols and Proanthocyanins.",9233
Wine_correlation_heatmap.png,"Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Ash and Proanthocyanins.",9234
Wine_correlation_heatmap.png,"Variables Alcohol and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Ash and Total phenols.",9235
Wine_correlation_heatmap.png,"Variables Proanthocyanins and Hue are redundant, but we can’t say the same for the pair Alcohol and Flavanoids.",9236
Wine_correlation_heatmap.png,"Variables Hue and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Malic acid and Total phenols.",9237
Wine_correlation_heatmap.png,"Variables Alcohol and Total phenols are redundant, but we can’t say the same for the pair Proanthocyanins and OD280-OD315 of diluted wines.",9238
Wine_correlation_heatmap.png,"Variables Ash and Total phenols are redundant, but we can’t say the same for the pair Alcohol and Nonflavanoid phenols.",9239
Wine_correlation_heatmap.png,"Variables Total phenols and Color intensity are redundant, but we can’t say the same for the pair Flavanoids and OD280-OD315 of diluted wines.",9240
Wine_correlation_heatmap.png,"Variables Ash and Alcalinity of ash are redundant, but we can’t say the same for the pair Alcohol and Nonflavanoid phenols.",9241
Wine_correlation_heatmap.png,"Variables Alcohol and Color intensity are redundant, but we can’t say the same for the pair Total phenols and Flavanoids.",9242
Wine_correlation_heatmap.png,"Variables Malic acid and Flavanoids are redundant, but we can’t say the same for the pair Color intensity and OD280-OD315 of diluted wines.",9243
Wine_correlation_heatmap.png,"Variables Ash and Color intensity are redundant, but we can’t say the same for the pair Alcohol and Total phenols.",9244
Wine_correlation_heatmap.png,The variable Class can be discarded without risking losing information.,9245
Wine_correlation_heatmap.png,The variable Alcohol can be discarded without risking losing information.,9246
Wine_correlation_heatmap.png,The variable Malic acid can be discarded without risking losing information.,9247
Wine_correlation_heatmap.png,The variable Ash can be discarded without risking losing information.,9248
Wine_correlation_heatmap.png,The variable Alcalinity of ash can be discarded without risking losing information.,9249
Wine_correlation_heatmap.png,The variable Total phenols can be discarded without risking losing information.,9250
Wine_correlation_heatmap.png,The variable Flavanoids can be discarded without risking losing information.,9251
Wine_correlation_heatmap.png,The variable Nonflavanoid phenols can be discarded without risking losing information.,9252
Wine_correlation_heatmap.png,The variable Proanthocyanins can be discarded without risking losing information.,9253
Wine_correlation_heatmap.png,The variable Color intensity can be discarded without risking losing information.,9254
Wine_correlation_heatmap.png,The variable Hue can be discarded without risking losing information.,9255
Wine_correlation_heatmap.png,The variable OD280-OD315 of diluted wines can be discarded without risking losing information.,9256
Wine_correlation_heatmap.png,One of the variables Alcohol or Class can be discarded without losing information.,9257
Wine_correlation_heatmap.png,One of the variables Malic acid or Class can be discarded without losing information.,9258
Wine_correlation_heatmap.png,One of the variables Ash or Class can be discarded without losing information.,9259
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Class can be discarded without losing information.,9260
Wine_correlation_heatmap.png,One of the variables Total phenols or Class can be discarded without losing information.,9261
Wine_correlation_heatmap.png,One of the variables Flavanoids or Class can be discarded without losing information.,9262
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Class can be discarded without losing information.,9263
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Class can be discarded without losing information.,9264
Wine_correlation_heatmap.png,One of the variables Color intensity or Class can be discarded without losing information.,9265
Wine_correlation_heatmap.png,One of the variables Hue or Class can be discarded without losing information.,9266
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Class can be discarded without losing information.,9267
Wine_correlation_heatmap.png,One of the variables Class or Alcohol can be discarded without losing information.,9268
Wine_correlation_heatmap.png,One of the variables Malic acid or Alcohol can be discarded without losing information.,9269
Wine_correlation_heatmap.png,One of the variables Ash or Alcohol can be discarded without losing information.,9270
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Alcohol can be discarded without losing information.,9271
Wine_correlation_heatmap.png,One of the variables Total phenols or Alcohol can be discarded without losing information.,9272
Wine_correlation_heatmap.png,One of the variables Flavanoids or Alcohol can be discarded without losing information.,9273
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Alcohol can be discarded without losing information.,9274
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Alcohol can be discarded without losing information.,9275
Wine_correlation_heatmap.png,One of the variables Color intensity or Alcohol can be discarded without losing information.,9276
Wine_correlation_heatmap.png,One of the variables Hue or Alcohol can be discarded without losing information.,9277
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Alcohol can be discarded without losing information.,9278
Wine_correlation_heatmap.png,One of the variables Class or Malic acid can be discarded without losing information.,9279
Wine_correlation_heatmap.png,One of the variables Alcohol or Malic acid can be discarded without losing information.,9280
Wine_correlation_heatmap.png,One of the variables Ash or Malic acid can be discarded without losing information.,9281
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Malic acid can be discarded without losing information.,9282
Wine_correlation_heatmap.png,One of the variables Total phenols or Malic acid can be discarded without losing information.,9283
Wine_correlation_heatmap.png,One of the variables Flavanoids or Malic acid can be discarded without losing information.,9284
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Malic acid can be discarded without losing information.,9285
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Malic acid can be discarded without losing information.,9286
Wine_correlation_heatmap.png,One of the variables Color intensity or Malic acid can be discarded without losing information.,9287
Wine_correlation_heatmap.png,One of the variables Hue or Malic acid can be discarded without losing information.,9288
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Malic acid can be discarded without losing information.,9289
Wine_correlation_heatmap.png,One of the variables Class or Ash can be discarded without losing information.,9290
Wine_correlation_heatmap.png,One of the variables Alcohol or Ash can be discarded without losing information.,9291
Wine_correlation_heatmap.png,One of the variables Malic acid or Ash can be discarded without losing information.,9292
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Ash can be discarded without losing information.,9293
Wine_correlation_heatmap.png,One of the variables Total phenols or Ash can be discarded without losing information.,9294
Wine_correlation_heatmap.png,One of the variables Flavanoids or Ash can be discarded without losing information.,9295
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Ash can be discarded without losing information.,9296
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Ash can be discarded without losing information.,9297
Wine_correlation_heatmap.png,One of the variables Color intensity or Ash can be discarded without losing information.,9298
Wine_correlation_heatmap.png,One of the variables Hue or Ash can be discarded without losing information.,9299
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Ash can be discarded without losing information.,9300
Wine_correlation_heatmap.png,One of the variables Class or Alcalinity of ash can be discarded without losing information.,9301
Wine_correlation_heatmap.png,One of the variables Alcohol or Alcalinity of ash can be discarded without losing information.,9302
Wine_correlation_heatmap.png,One of the variables Malic acid or Alcalinity of ash can be discarded without losing information.,9303
Wine_correlation_heatmap.png,One of the variables Ash or Alcalinity of ash can be discarded without losing information.,9304
Wine_correlation_heatmap.png,One of the variables Total phenols or Alcalinity of ash can be discarded without losing information.,9305
Wine_correlation_heatmap.png,One of the variables Flavanoids or Alcalinity of ash can be discarded without losing information.,9306
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Alcalinity of ash can be discarded without losing information.,9307
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Alcalinity of ash can be discarded without losing information.,9308
Wine_correlation_heatmap.png,One of the variables Color intensity or Alcalinity of ash can be discarded without losing information.,9309
Wine_correlation_heatmap.png,One of the variables Hue or Alcalinity of ash can be discarded without losing information.,9310
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Alcalinity of ash can be discarded without losing information.,9311
Wine_correlation_heatmap.png,One of the variables Class or Total phenols can be discarded without losing information.,9312
Wine_correlation_heatmap.png,One of the variables Alcohol or Total phenols can be discarded without losing information.,9313
Wine_correlation_heatmap.png,One of the variables Malic acid or Total phenols can be discarded without losing information.,9314
Wine_correlation_heatmap.png,One of the variables Ash or Total phenols can be discarded without losing information.,9315
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Total phenols can be discarded without losing information.,9316
Wine_correlation_heatmap.png,One of the variables Flavanoids or Total phenols can be discarded without losing information.,9317
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Total phenols can be discarded without losing information.,9318
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Total phenols can be discarded without losing information.,9319
Wine_correlation_heatmap.png,One of the variables Color intensity or Total phenols can be discarded without losing information.,9320
Wine_correlation_heatmap.png,One of the variables Hue or Total phenols can be discarded without losing information.,9321
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Total phenols can be discarded without losing information.,9322
Wine_correlation_heatmap.png,One of the variables Class or Flavanoids can be discarded without losing information.,9323
Wine_correlation_heatmap.png,One of the variables Alcohol or Flavanoids can be discarded without losing information.,9324
Wine_correlation_heatmap.png,One of the variables Malic acid or Flavanoids can be discarded without losing information.,9325
Wine_correlation_heatmap.png,One of the variables Ash or Flavanoids can be discarded without losing information.,9326
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Flavanoids can be discarded without losing information.,9327
Wine_correlation_heatmap.png,One of the variables Total phenols or Flavanoids can be discarded without losing information.,9328
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Flavanoids can be discarded without losing information.,9329
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Flavanoids can be discarded without losing information.,9330
Wine_correlation_heatmap.png,One of the variables Color intensity or Flavanoids can be discarded without losing information.,9331
Wine_correlation_heatmap.png,One of the variables Hue or Flavanoids can be discarded without losing information.,9332
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Flavanoids can be discarded without losing information.,9333
Wine_correlation_heatmap.png,One of the variables Class or Nonflavanoid phenols can be discarded without losing information.,9334
Wine_correlation_heatmap.png,One of the variables Alcohol or Nonflavanoid phenols can be discarded without losing information.,9335
Wine_correlation_heatmap.png,One of the variables Malic acid or Nonflavanoid phenols can be discarded without losing information.,9336
Wine_correlation_heatmap.png,One of the variables Ash or Nonflavanoid phenols can be discarded without losing information.,9337
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Nonflavanoid phenols can be discarded without losing information.,9338
Wine_correlation_heatmap.png,One of the variables Total phenols or Nonflavanoid phenols can be discarded without losing information.,9339
Wine_correlation_heatmap.png,One of the variables Flavanoids or Nonflavanoid phenols can be discarded without losing information.,9340
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Nonflavanoid phenols can be discarded without losing information.,9341
Wine_correlation_heatmap.png,One of the variables Color intensity or Nonflavanoid phenols can be discarded without losing information.,9342
Wine_correlation_heatmap.png,One of the variables Hue or Nonflavanoid phenols can be discarded without losing information.,9343
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Nonflavanoid phenols can be discarded without losing information.,9344
Wine_correlation_heatmap.png,One of the variables Class or Proanthocyanins can be discarded without losing information.,9345
Wine_correlation_heatmap.png,One of the variables Alcohol or Proanthocyanins can be discarded without losing information.,9346
Wine_correlation_heatmap.png,One of the variables Malic acid or Proanthocyanins can be discarded without losing information.,9347
Wine_correlation_heatmap.png,One of the variables Ash or Proanthocyanins can be discarded without losing information.,9348
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Proanthocyanins can be discarded without losing information.,9349
Wine_correlation_heatmap.png,One of the variables Total phenols or Proanthocyanins can be discarded without losing information.,9350
Wine_correlation_heatmap.png,One of the variables Flavanoids or Proanthocyanins can be discarded without losing information.,9351
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Proanthocyanins can be discarded without losing information.,9352
Wine_correlation_heatmap.png,One of the variables Color intensity or Proanthocyanins can be discarded without losing information.,9353
Wine_correlation_heatmap.png,One of the variables Hue or Proanthocyanins can be discarded without losing information.,9354
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Proanthocyanins can be discarded without losing information.,9355
Wine_correlation_heatmap.png,One of the variables Class or Color intensity can be discarded without losing information.,9356
Wine_correlation_heatmap.png,One of the variables Alcohol or Color intensity can be discarded without losing information.,9357
Wine_correlation_heatmap.png,One of the variables Malic acid or Color intensity can be discarded without losing information.,9358
Wine_correlation_heatmap.png,One of the variables Ash or Color intensity can be discarded without losing information.,9359
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Color intensity can be discarded without losing information.,9360
Wine_correlation_heatmap.png,One of the variables Total phenols or Color intensity can be discarded without losing information.,9361
Wine_correlation_heatmap.png,One of the variables Flavanoids or Color intensity can be discarded without losing information.,9362
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Color intensity can be discarded without losing information.,9363
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Color intensity can be discarded without losing information.,9364
Wine_correlation_heatmap.png,One of the variables Hue or Color intensity can be discarded without losing information.,9365
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Color intensity can be discarded without losing information.,9366
Wine_correlation_heatmap.png,One of the variables Class or Hue can be discarded without losing information.,9367
Wine_correlation_heatmap.png,One of the variables Alcohol or Hue can be discarded without losing information.,9368
Wine_correlation_heatmap.png,One of the variables Malic acid or Hue can be discarded without losing information.,9369
Wine_correlation_heatmap.png,One of the variables Ash or Hue can be discarded without losing information.,9370
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Hue can be discarded without losing information.,9371
Wine_correlation_heatmap.png,One of the variables Total phenols or Hue can be discarded without losing information.,9372
Wine_correlation_heatmap.png,One of the variables Flavanoids or Hue can be discarded without losing information.,9373
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Hue can be discarded without losing information.,9374
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Hue can be discarded without losing information.,9375
Wine_correlation_heatmap.png,One of the variables Color intensity or Hue can be discarded without losing information.,9376
Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Hue can be discarded without losing information.,9377
Wine_correlation_heatmap.png,One of the variables Class or OD280-OD315 of diluted wines can be discarded without losing information.,9378
Wine_correlation_heatmap.png,One of the variables Alcohol or OD280-OD315 of diluted wines can be discarded without losing information.,9379
Wine_correlation_heatmap.png,One of the variables Malic acid or OD280-OD315 of diluted wines can be discarded without losing information.,9380
Wine_correlation_heatmap.png,One of the variables Ash or OD280-OD315 of diluted wines can be discarded without losing information.,9381
Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or OD280-OD315 of diluted wines can be discarded without losing information.,9382
Wine_correlation_heatmap.png,One of the variables Total phenols or OD280-OD315 of diluted wines can be discarded without losing information.,9383
Wine_correlation_heatmap.png,One of the variables Flavanoids or OD280-OD315 of diluted wines can be discarded without losing information.,9384
Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or OD280-OD315 of diluted wines can be discarded without losing information.,9385
Wine_correlation_heatmap.png,One of the variables Proanthocyanins or OD280-OD315 of diluted wines can be discarded without losing information.,9386
Wine_correlation_heatmap.png,One of the variables Color intensity or OD280-OD315 of diluted wines can be discarded without losing information.,9387
Wine_correlation_heatmap.png,One of the variables Hue or OD280-OD315 of diluted wines can be discarded without losing information.,9388
Wine_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,9389
Wine_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,9390
Wine_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,9391
Wine_histograms_numeric.png,At least 50 of the variables present outliers.,9392
Wine_boxplots.png,At least 60 of the variables present outliers.,9393
Wine_boxplots.png,At least 75 of the variables present outliers.,9394
Wine_boxplots.png,At least 85 of the variables present outliers.,9395
Wine_boxplots.png,Variable Class presents some outliers.,9396
Wine_histograms_numeric.png,Variable Alcohol presents some outliers.,9397
Wine_boxplots.png,Variable Malic acid presents some outliers.,9398
Wine_boxplots.png,Variable Ash presents some outliers.,9399
Wine_boxplots.png,Variable Alcalinity of ash presents some outliers.,9400
Wine_boxplots.png,Variable Total phenols presents some outliers.,9401
Wine_histograms_numeric.png,Variable Flavanoids presents some outliers.,9402
Wine_histograms_numeric.png,Variable Nonflavanoid phenols presents some outliers.,9403
Wine_histograms_numeric.png,Variable Proanthocyanins presents some outliers.,9404
Wine_histograms_numeric.png,Variable Color intensity presents some outliers.,9405
Wine_boxplots.png,Variable Hue presents some outliers.,9406
Wine_boxplots.png,Variable OD280-OD315 of diluted wines presents some outliers.,9407
Wine_histograms_numeric.png,Variable Class doesn’t have any outliers.,9408
Wine_histograms_numeric.png,Variable Alcohol doesn’t have any outliers.,9409
Wine_histograms_numeric.png,Variable Malic acid doesn’t have any outliers.,9410
Wine_histograms_numeric.png,Variable Ash doesn’t have any outliers.,9411
Wine_histograms_numeric.png,Variable Alcalinity of ash doesn’t have any outliers.,9412
Wine_histograms_numeric.png,Variable Total phenols doesn’t have any outliers.,9413
Wine_histograms_numeric.png,Variable Flavanoids doesn’t have any outliers.,9414
Wine_boxplots.png,Variable Nonflavanoid phenols doesn’t have any outliers.,9415
Wine_histograms_numeric.png,Variable Proanthocyanins doesn’t have any outliers.,9416
Wine_boxplots.png,Variable Color intensity doesn’t have any outliers.,9417
Wine_histograms_numeric.png,Variable Hue doesn’t have any outliers.,9418
Wine_histograms_numeric.png,Variable OD280-OD315 of diluted wines doesn’t have any outliers.,9419
Wine_histograms_numeric.png,Variable Class shows some outlier values.,9420
Wine_histograms_numeric.png,Variable Alcohol shows some outlier values.,9421
Wine_histograms_numeric.png,Variable Malic acid shows some outlier values.,9422
Wine_histograms_numeric.png,Variable Ash shows some outlier values.,9423
Wine_histograms_numeric.png,Variable Alcalinity of ash shows some outlier values.,9424
Wine_boxplots.png,Variable Total phenols shows some outlier values.,9425
Wine_boxplots.png,Variable Flavanoids shows some outlier values.,9426
Wine_histograms_numeric.png,Variable Nonflavanoid phenols shows some outlier values.,9427
Wine_histograms_numeric.png,Variable Proanthocyanins shows some outlier values.,9428
Wine_boxplots.png,Variable Color intensity shows some outlier values.,9429
Wine_boxplots.png,Variable Hue shows some outlier values.,9430
Wine_histograms_numeric.png,Variable OD280-OD315 of diluted wines shows some outlier values.,9431
Wine_boxplots.png,Variable Class shows a high number of outlier values.,9432
Wine_boxplots.png,Variable Alcohol shows a high number of outlier values.,9433
Wine_histograms_numeric.png,Variable Malic acid shows a high number of outlier values.,9434
Wine_boxplots.png,Variable Ash shows a high number of outlier values.,9435
Wine_histograms_numeric.png,Variable Alcalinity of ash shows a high number of outlier values.,9436
Wine_boxplots.png,Variable Total phenols shows a high number of outlier values.,9437
Wine_histograms_numeric.png,Variable Flavanoids shows a high number of outlier values.,9438
Wine_boxplots.png,Variable Nonflavanoid phenols shows a high number of outlier values.,9439
Wine_histograms_numeric.png,Variable Proanthocyanins shows a high number of outlier values.,9440
Wine_histograms_numeric.png,Variable Color intensity shows a high number of outlier values.,9441
Wine_boxplots.png,Variable Hue shows a high number of outlier values.,9442
Wine_histograms_numeric.png,Variable OD280-OD315 of diluted wines shows a high number of outlier values.,9443
Wine_boxplots.png,Outliers seem to be a problem in the dataset.,9444
Wine_histograms_numeric.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Class.",9445
Wine_boxplots.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Class.",9446
Wine_boxplots.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Class.",9447
Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Class.",9448
Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Class.",9449
Wine_boxplots.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Class.",9450
Wine_histograms_numeric.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Class.",9451
Wine_boxplots.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Class.",9452
Wine_boxplots.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Class.",9453
Wine_histograms_numeric.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Class.",9454
Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Class.",9455
Wine_histograms_numeric.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Alcohol.",9456
Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Alcohol.",9457
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Alcohol.",9458
Wine_boxplots.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Alcohol.",9459
Wine_histograms_numeric.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Alcohol.",9460
Wine_boxplots.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Alcohol.",9461
Wine_boxplots.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Alcohol.",9462
Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Alcohol.",9463
Wine_histograms_numeric.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Alcohol.",9464
Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Alcohol.",9465
Wine_histograms_numeric.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Alcohol.",9466
Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Malic acid.",9467
Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Malic acid.",9468
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Malic acid.",9469
Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Malic acid.",9470
Wine_histograms_numeric.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Malic acid.",9471
Wine_histograms_numeric.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Malic acid.",9472
Wine_histograms_numeric.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Malic acid.",9473
Wine_boxplots.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Malic acid.",9474
Wine_boxplots.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Malic acid.",9475
Wine_histograms_numeric.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Malic acid.",9476
Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Malic acid.",9477
Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Ash.",9478
Wine_histograms_numeric.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Ash.",9479
Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Ash.",9480
Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Ash.",9481
Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Ash.",9482
Wine_boxplots.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Ash.",9483
Wine_boxplots.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Ash.",9484
Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Ash.",9485
Wine_histograms_numeric.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Ash.",9486
Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Ash.",9487
Wine_histograms_numeric.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Ash.",9488
Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9489
Wine_histograms_numeric.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9490
Wine_boxplots.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9491
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9492
Wine_histograms_numeric.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9493
Wine_histograms_numeric.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9494
Wine_histograms_numeric.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9495
Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9496
Wine_boxplots.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9497
Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9498
Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9499
Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Total phenols.",9500
Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Total phenols.",9501
Wine_boxplots.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Total phenols.",9502
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Total phenols.",9503
Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Total phenols.",9504
Wine_boxplots.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Total phenols.",9505
Wine_boxplots.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Total phenols.",9506
Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Total phenols.",9507
Wine_histograms_numeric.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Total phenols.",9508
Wine_histograms_numeric.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Total phenols.",9509
Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Total phenols.",9510
Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9511
Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9512
Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9513
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9514
Wine_boxplots.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9515
Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9516
Wine_histograms_numeric.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9517
Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9518
Wine_boxplots.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9519
Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9520
Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9521
Wine_histograms_numeric.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9522
Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9523
Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9524
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9525
Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9526
Wine_histograms_numeric.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9527
Wine_boxplots.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9528
Wine_boxplots.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9529
Wine_boxplots.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9530
Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9531
Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9532
Wine_histograms_numeric.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9533
Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9534
Wine_boxplots.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9535
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9536
Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9537
Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9538
Wine_histograms_numeric.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9539
Wine_histograms_numeric.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9540
Wine_histograms_numeric.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9541
Wine_histograms_numeric.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9542
Wine_histograms_numeric.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9543
Wine_histograms_numeric.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Color intensity.",9544
Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Color intensity.",9545
Wine_boxplots.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Color intensity.",9546
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Color intensity.",9547
Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Color intensity.",9548
Wine_histograms_numeric.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Color intensity.",9549
Wine_boxplots.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Color intensity.",9550
Wine_boxplots.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Color intensity.",9551
Wine_boxplots.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Color intensity.",9552
Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Color intensity.",9553
Wine_histograms_numeric.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Color intensity.",9554
Wine_histograms_numeric.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Hue.",9555
Wine_histograms_numeric.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Hue.",9556
Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Hue.",9557
Wine_boxplots.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Hue.",9558
Wine_boxplots.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Hue.",9559
Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Hue.",9560
Wine_boxplots.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Hue.",9561
Wine_boxplots.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Hue.",9562
Wine_boxplots.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Hue.",9563
Wine_histograms_numeric.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable Hue.",9564
Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Hue.",9565
Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9566
Wine_histograms_numeric.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9567
Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9568
Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9569
Wine_boxplots.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9570
Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9571
Wine_histograms_numeric.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9572
Wine_histograms_numeric.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9573
Wine_boxplots.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9574
Wine_boxplots.png,"It is clear that variable Color intensity shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9575
Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9576
Wine_boxplots.png,Those boxplots show that the data is not normalized.,9577
Wine_histograms_numeric.png,Variable Class is balanced.,9578
Wine_boxplots.png,Variable Alcohol is balanced.,9579
Wine_boxplots.png,Variable Malic acid is balanced.,9580
Wine_histograms_numeric.png,Variable Ash is balanced.,9581
Wine_histograms_numeric.png,Variable Alcalinity of ash is balanced.,9582
Wine_boxplots.png,Variable Total phenols is balanced.,9583
Wine_boxplots.png,Variable Flavanoids is balanced.,9584
Wine_boxplots.png,Variable Nonflavanoid phenols is balanced.,9585
Wine_boxplots.png,Variable Proanthocyanins is balanced.,9586
Wine_histograms_numeric.png,Variable Color intensity is balanced.,9587
Wine_histograms_numeric.png,Variable Hue is balanced.,9588
Wine_boxplots.png,Variable OD280-OD315 of diluted wines is balanced.,9589
Wine_histograms.png,The variable Alcohol can be seen as ordinal without losing information.,9590
Wine_histograms.png,The variable Malic acid can be seen as ordinal without losing information.,9591
Wine_histograms.png,The variable Ash can be seen as ordinal without losing information.,9592
Wine_histograms.png,The variable Alcalinity of ash can be seen as ordinal without losing information.,9593
Wine_histograms.png,The variable Total phenols can be seen as ordinal without losing information.,9594
Wine_histograms.png,The variable Flavanoids can be seen as ordinal without losing information.,9595
Wine_histograms.png,The variable Nonflavanoid phenols can be seen as ordinal without losing information.,9596
Wine_histograms.png,The variable Proanthocyanins can be seen as ordinal without losing information.,9597
Wine_histograms.png,The variable Color intensity can be seen as ordinal without losing information.,9598
Wine_histograms.png,The variable Hue can be seen as ordinal without losing information.,9599
Wine_histograms.png,The variable OD280-OD315 of diluted wines can be seen as ordinal without losing information.,9600
Wine_histograms.png,The variable Alcohol can be seen as ordinal.,9601
Wine_histograms.png,The variable Malic acid can be seen as ordinal.,9602
Wine_histograms.png,The variable Ash can be seen as ordinal.,9603
Wine_histograms.png,The variable Alcalinity of ash can be seen as ordinal.,9604
Wine_histograms.png,The variable Total phenols can be seen as ordinal.,9605
Wine_histograms.png,The variable Flavanoids can be seen as ordinal.,9606
Wine_histograms.png,The variable Nonflavanoid phenols can be seen as ordinal.,9607
Wine_histograms.png,The variable Proanthocyanins can be seen as ordinal.,9608
Wine_histograms.png,The variable Color intensity can be seen as ordinal.,9609
Wine_histograms.png,The variable Hue can be seen as ordinal.,9610
Wine_histograms.png,The variable OD280-OD315 of diluted wines can be seen as ordinal.,9611
Wine_histograms.png,"All variables, but the class, should be dealt with as numeric.",9612
Wine_histograms.png,"All variables, but the class, should be dealt with as binary.",9613
Wine_histograms.png,"All variables, but the class, should be dealt with as date.",9614
Wine_histograms.png,"All variables, but the class, should be dealt with as symbolic.",9615
Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 71.,9616
Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 95.,9617
Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 99.,9618
Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 90.,9619
Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 29.,9620
Wine_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,9621
Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",9622
Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",9623
Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",9624
Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",9625
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (A,B), as 0.",9626
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 0.",9627
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 0.",9628
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 0.",9629
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",9630
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",9631
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",9632
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",9633
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 214.",9634
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 214.",9635
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 214.",9636
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 214.",9637
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 214.",9638
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 214.",9639
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 214.",9640
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 214.",9641
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 436.",9642
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 436.",9643
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 436.",9644
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 436.",9645
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 436.",9646
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 436.",9647
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 436.",9648
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 436.",9649
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 179.",9650
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 179.",9651
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 179.",9652
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 179.",9653
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 179.",9654
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 179.",9655
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 179.",9656
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 179.",9657
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 131.",9658
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 131.",9659
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 131.",9660
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 131.",9661
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 131.",9662
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 131.",9663
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 131.",9664
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 131.",9665
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (A,B) as 0.",9666
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (not A, B) as 0.",9667
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (A, not B) as 0.",9668
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (not A, not B) as 0.",9669
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (A,B) as 1.",9670
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (not A, B) as 1.",9671
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (A, not B) as 1.",9672
BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (not A, not B) as 1.",9673
BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 668 episodes.,9674
BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1297 episodes.,9675
BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1448 episodes.,9676
BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1124 episodes.,9677
BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1164 episodes.,9678
BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 88 estimators.,9679
BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 70 estimators.,9680
BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 117 estimators.,9681
BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 99 estimators.,9682
BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 95 estimators.,9683
BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 132 estimators.,9684
BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 88 estimators.,9685
BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 113 estimators.,9686
BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 125 estimators.,9687
BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 82 estimators.,9688
BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,9689
BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,9690
BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,9691
BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,9692
BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,9693
BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,9694
BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,9695
BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,9696
BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,9697
BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,9698
BankNoteAuthentication_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,9699
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than its accuracy.,9700
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than its accuracy.,9701
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,9702
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than its accuracy.,9703
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than its accuracy.,9704
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,9705
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than its recall.,9706
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than its recall.,9707
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than its recall.,9708
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than its recall.,9709
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than its recall.,9710
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than its recall.,9711
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than its precision.,9712
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than its precision.,9713
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than its precision.,9714
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than its precision.,9715
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than its precision.,9716
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than its precision.,9717
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,9718
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than its specificity.,9719
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than its specificity.,9720
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,9721
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than its specificity.,9722
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than its specificity.,9723
BankNoteAuthentication_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,9724
BankNoteAuthentication_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,9725
BankNoteAuthentication_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,9726
BankNoteAuthentication_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,9727
BankNoteAuthentication_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,9728
BankNoteAuthentication_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,9729
BankNoteAuthentication_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,9730
BankNoteAuthentication_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,9731
BankNoteAuthentication_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,9732
BankNoteAuthentication_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,9733
BankNoteAuthentication_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,9734
BankNoteAuthentication_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,9735
BankNoteAuthentication_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,9736
BankNoteAuthentication_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,9737
BankNoteAuthentication_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,9738
BankNoteAuthentication_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,9739
BankNoteAuthentication_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,9740
BankNoteAuthentication_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,9741
BankNoteAuthentication_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,9742
BankNoteAuthentication_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,9743
BankNoteAuthentication_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,9744
BankNoteAuthentication_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,9745
BankNoteAuthentication_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,9746
BankNoteAuthentication_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,9747
BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 48.,9748
BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 29.,9749
BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 45.,9750
BankNoteAuthentication_decision_tree.png,The number of False Negatives reported in the same tree is 23.,9751
BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 40.,9752
BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 39.,9753
BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 20.,9754
BankNoteAuthentication_decision_tree.png,The number of False Negatives reported in the same tree is 27.,9755
BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 28.,9756
BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 21.,9757
BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 38.,9758
BankNoteAuthentication_decision_tree.png,The number of False Negatives reported in the same tree is 37.,9759
BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 43.,9760
BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 14.,9761
BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 33.,9762
BankNoteAuthentication_decision_tree.png,The number of False Negatives reported in the same tree is 35.,9763
BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 19.,9764
BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 15.,9765
BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 50.,9766
BankNoteAuthentication_decision_tree.png,The number of False Negatives reported in the same tree is 17.,9767
BankNoteAuthentication_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,9768
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,9769
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,9770
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,9771
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,9772
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,9773
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,9774
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,9775
BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,9776
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,9777
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,9778
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,9779
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,9780
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,9781
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,9782
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,9783
BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,9784
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 63%.,9785
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 77%.,9786
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 75%.,9787
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 70%.,9788
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 90%.,9789
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 89%.,9790
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 62%.,9791
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 79%.,9792
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 82%.,9793
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 84%.,9794
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 65%.,9795
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 87%.,9796
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 60%.,9797
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 69%.,9798
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 61%.,9799
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 81%.,9800
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 67%.,9801
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 72%.,9802
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 78%.,9803
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 71%.,9804
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 83%.,9805
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 85%.,9806
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 64%.,9807
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 86%.,9808
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 73%.,9809
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 68%.,9810
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 80%.,9811
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 66%.,9812
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 76%.,9813
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 88%.,9814
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 74%.,9815
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 64%.,9816
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 71%.,9817
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 87%.,9818
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 73%.,9819
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 60%.,9820
BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 74%.,9821
BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 77%.,9822
BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 71%.,9823
BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 74%.,9824
BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",9825
BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",9826
BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",9827
BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",9828
BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",9829
BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",9830
BankNoteAuthentication_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,9831
BankNoteAuthentication_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,9832
BankNoteAuthentication_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,9833
BankNoteAuthentication_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,9834
BankNoteAuthentication_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,9835
BankNoteAuthentication_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,9836
BankNoteAuthentication_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,9837
BankNoteAuthentication_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,9838
BankNoteAuthentication_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,9839
BankNoteAuthentication_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,9840
BankNoteAuthentication_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,9841
BankNoteAuthentication_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,9842
BankNoteAuthentication_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,9843
BankNoteAuthentication_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,9844
BankNoteAuthentication_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,9845
BankNoteAuthentication_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,9846
BankNoteAuthentication_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,9847
BankNoteAuthentication_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,9848
BankNoteAuthentication_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,9849
BankNoteAuthentication_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,9850
BankNoteAuthentication_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,9851
BankNoteAuthentication_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,9852
BankNoteAuthentication_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,9853
BankNoteAuthentication_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,9854
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 2.,9855
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 2.,9856
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 3.,9857
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 3.,9858
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 4.,9859
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 4.,9860
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 5.,9861
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 5.,9862
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 6.,9863
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 6.,9864
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 7.,9865
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 7.,9866
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 8.,9867
BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 8.,9868
BankNoteAuthentication_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",9869
BankNoteAuthentication_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",9870
BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",9871
BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",9872
BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",9873
BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",9874
BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",9875
BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",9876
BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",9877
BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",9878
BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",9879
BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",9880
BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",9881
BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",9882
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,9883
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,9884
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,9885
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,9886
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,9887
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,9888
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,9889
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,9890
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,9891
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,9892
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,9893
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,9894
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,9895
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,9896
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,9897
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,9898
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,9899
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,9900
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,9901
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,9902
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,9903
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,9904
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,9905
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,9906
BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,9907
BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,9908
BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,9909
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable skewness previously than variable variance.,9910
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable curtosis previously than variable variance.,9911
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable entropy previously than variable variance.,9912
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable variance previously than variable skewness.,9913
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable curtosis previously than variable skewness.,9914
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable entropy previously than variable skewness.,9915
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable variance previously than variable curtosis.,9916
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable skewness previously than variable curtosis.,9917
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable entropy previously than variable curtosis.,9918
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable variance previously than variable entropy.,9919
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable skewness previously than variable entropy.,9920
BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable curtosis previously than variable entropy.,9921
BankNoteAuthentication_pca.png,The first 2 principal components are enough for explaining half the data variance.,9922
BankNoteAuthentication_pca.png,The first 3 principal components are enough for explaining half the data variance.,9923
BankNoteAuthentication_pca.png,The first 4 principal components are enough for explaining half the data variance.,9924
BankNoteAuthentication_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,9925
BankNoteAuthentication_correlation_heatmap.png,Removing variable variance might improve the training of decision trees .,9926
BankNoteAuthentication_correlation_heatmap.png,Removing variable skewness might improve the training of decision trees .,9927
BankNoteAuthentication_correlation_heatmap.png,Removing variable curtosis might improve the training of decision trees .,9928
BankNoteAuthentication_correlation_heatmap.png,Removing variable entropy might improve the training of decision trees .,9929
BankNoteAuthentication_histograms.png,"Not knowing the semantics of variance variable, dummification could have been a more adequate codification.",9930
BankNoteAuthentication_histograms.png,"Not knowing the semantics of skewness variable, dummification could have been a more adequate codification.",9931
BankNoteAuthentication_histograms.png,"Not knowing the semantics of curtosis variable, dummification could have been a more adequate codification.",9932
BankNoteAuthentication_histograms.png,"Not knowing the semantics of entropy variable, dummification could have been a more adequate codification.",9933
BankNoteAuthentication_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,9934
BankNoteAuthentication_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",9935
BankNoteAuthentication_histograms.png,"Given the usual semantics of variance variable, dummification would have been a better codification.",9936
BankNoteAuthentication_histograms.png,"Given the usual semantics of skewness variable, dummification would have been a better codification.",9937
BankNoteAuthentication_histograms.png,"Given the usual semantics of curtosis variable, dummification would have been a better codification.",9938
BankNoteAuthentication_histograms.png,"Given the usual semantics of entropy variable, dummification would have been a better codification.",9939
BankNoteAuthentication_histograms.png,The variable variance can be coded as ordinal without losing information.,9940
BankNoteAuthentication_histograms.png,The variable skewness can be coded as ordinal without losing information.,9941
BankNoteAuthentication_histograms.png,The variable curtosis can be coded as ordinal without losing information.,9942
BankNoteAuthentication_histograms.png,The variable entropy can be coded as ordinal without losing information.,9943
BankNoteAuthentication_histograms.png,"Considering the common semantics for variance variable, dummification would be the most adequate encoding.",9944
BankNoteAuthentication_histograms.png,"Considering the common semantics for skewness variable, dummification would be the most adequate encoding.",9945
BankNoteAuthentication_histograms.png,"Considering the common semantics for curtosis variable, dummification would be the most adequate encoding.",9946
BankNoteAuthentication_histograms.png,"Considering the common semantics for entropy variable, dummification would be the most adequate encoding.",9947
BankNoteAuthentication_histograms.png,"Considering the common semantics for skewness and variance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9948
BankNoteAuthentication_histograms.png,"Considering the common semantics for curtosis and variance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9949
BankNoteAuthentication_histograms.png,"Considering the common semantics for entropy and variance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9950
BankNoteAuthentication_histograms.png,"Considering the common semantics for variance and skewness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9951
BankNoteAuthentication_histograms.png,"Considering the common semantics for curtosis and skewness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9952
BankNoteAuthentication_histograms.png,"Considering the common semantics for entropy and skewness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9953
BankNoteAuthentication_histograms.png,"Considering the common semantics for variance and curtosis variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9954
BankNoteAuthentication_histograms.png,"Considering the common semantics for skewness and curtosis variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9955
BankNoteAuthentication_histograms.png,"Considering the common semantics for entropy and curtosis variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9956
BankNoteAuthentication_histograms.png,"Considering the common semantics for variance and entropy variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9957
BankNoteAuthentication_histograms.png,"Considering the common semantics for skewness and entropy variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9958
BankNoteAuthentication_histograms.png,"Considering the common semantics for curtosis and entropy variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9959
BankNoteAuthentication_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,9960
BankNoteAuthentication_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,9961
BankNoteAuthentication_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,9962
BankNoteAuthentication_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",9963
BankNoteAuthentication_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",9964
BankNoteAuthentication_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",9965
BankNoteAuthentication_correlation_heatmap.png,Variables skewness and variance seem to be useful for classification tasks.,9966
BankNoteAuthentication_correlation_heatmap.png,Variables curtosis and variance seem to be useful for classification tasks.,9967
BankNoteAuthentication_correlation_heatmap.png,Variables entropy and variance seem to be useful for classification tasks.,9968
BankNoteAuthentication_correlation_heatmap.png,Variables variance and skewness seem to be useful for classification tasks.,9969
BankNoteAuthentication_correlation_heatmap.png,Variables curtosis and skewness seem to be useful for classification tasks.,9970
BankNoteAuthentication_correlation_heatmap.png,Variables entropy and skewness seem to be useful for classification tasks.,9971
BankNoteAuthentication_correlation_heatmap.png,Variables variance and curtosis seem to be useful for classification tasks.,9972
BankNoteAuthentication_correlation_heatmap.png,Variables skewness and curtosis seem to be useful for classification tasks.,9973
BankNoteAuthentication_correlation_heatmap.png,Variables entropy and curtosis seem to be useful for classification tasks.,9974
BankNoteAuthentication_correlation_heatmap.png,Variables variance and entropy seem to be useful for classification tasks.,9975
BankNoteAuthentication_correlation_heatmap.png,Variables skewness and entropy seem to be useful for classification tasks.,9976
BankNoteAuthentication_correlation_heatmap.png,Variables curtosis and entropy seem to be useful for classification tasks.,9977
BankNoteAuthentication_correlation_heatmap.png,Variable variance seems to be relevant for the majority of mining tasks.,9978
BankNoteAuthentication_correlation_heatmap.png,Variable skewness seems to be relevant for the majority of mining tasks.,9979
BankNoteAuthentication_correlation_heatmap.png,Variable curtosis seems to be relevant for the majority of mining tasks.,9980
BankNoteAuthentication_correlation_heatmap.png,Variable entropy seems to be relevant for the majority of mining tasks.,9981
BankNoteAuthentication_decision_tree.png,Variable variance is one of the most relevant variables.,9982
BankNoteAuthentication_decision_tree.png,Variable skewness is one of the most relevant variables.,9983
BankNoteAuthentication_decision_tree.png,Variable curtosis is one of the most relevant variables.,9984
BankNoteAuthentication_decision_tree.png,Variable entropy is one of the most relevant variables.,9985
BankNoteAuthentication_decision_tree.png,It is possible to state that variance is the first most discriminative variable regarding the class.,9986
BankNoteAuthentication_decision_tree.png,It is possible to state that skewness is the first most discriminative variable regarding the class.,9987
BankNoteAuthentication_decision_tree.png,It is possible to state that curtosis is the first most discriminative variable regarding the class.,9988
BankNoteAuthentication_decision_tree.png,It is possible to state that entropy is the first most discriminative variable regarding the class.,9989
BankNoteAuthentication_decision_tree.png,It is possible to state that variance is the second most discriminative variable regarding the class.,9990
BankNoteAuthentication_decision_tree.png,It is possible to state that skewness is the second most discriminative variable regarding the class.,9991
BankNoteAuthentication_decision_tree.png,It is possible to state that curtosis is the second most discriminative variable regarding the class.,9992
BankNoteAuthentication_decision_tree.png,It is possible to state that entropy is the second most discriminative variable regarding the class.,9993
BankNoteAuthentication_decision_tree.png,"The variable variance discriminates between the target values, as shown in the decision tree.",9994
BankNoteAuthentication_decision_tree.png,"The variable skewness discriminates between the target values, as shown in the decision tree.",9995
BankNoteAuthentication_decision_tree.png,"The variable curtosis discriminates between the target values, as shown in the decision tree.",9996
BankNoteAuthentication_decision_tree.png,"The variable entropy discriminates between the target values, as shown in the decision tree.",9997
BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the two most relevant features.,9998
BankNoteAuthentication_decision_tree.png,The variable skewness seems to be one of the two most relevant features.,9999
BankNoteAuthentication_decision_tree.png,The variable curtosis seems to be one of the two most relevant features.,10000
BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the two most relevant features.,10001
BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the three most relevant features.,10002
BankNoteAuthentication_decision_tree.png,The variable skewness seems to be one of the three most relevant features.,10003
BankNoteAuthentication_decision_tree.png,The variable curtosis seems to be one of the three most relevant features.,10004
BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the three most relevant features.,10005
BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the four most relevant features.,10006
BankNoteAuthentication_decision_tree.png,The variable skewness seems to be one of the four most relevant features.,10007
BankNoteAuthentication_decision_tree.png,The variable curtosis seems to be one of the four most relevant features.,10008
BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the four most relevant features.,10009
BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the five most relevant features.,10010
BankNoteAuthentication_decision_tree.png,The variable skewness seems to be one of the five most relevant features.,10011
BankNoteAuthentication_decision_tree.png,The variable curtosis seems to be one of the five most relevant features.,10012
BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the five most relevant features.,10013
BankNoteAuthentication_decision_tree.png,It is clear that variable variance is one of the two most relevant features.,10014
BankNoteAuthentication_decision_tree.png,It is clear that variable skewness is one of the two most relevant features.,10015
BankNoteAuthentication_decision_tree.png,It is clear that variable curtosis is one of the two most relevant features.,10016
BankNoteAuthentication_decision_tree.png,It is clear that variable entropy is one of the two most relevant features.,10017
BankNoteAuthentication_decision_tree.png,It is clear that variable variance is one of the three most relevant features.,10018
BankNoteAuthentication_decision_tree.png,It is clear that variable skewness is one of the three most relevant features.,10019
BankNoteAuthentication_decision_tree.png,It is clear that variable curtosis is one of the three most relevant features.,10020
BankNoteAuthentication_decision_tree.png,It is clear that variable entropy is one of the three most relevant features.,10021
BankNoteAuthentication_decision_tree.png,It is clear that variable variance is one of the four most relevant features.,10022
BankNoteAuthentication_decision_tree.png,It is clear that variable skewness is one of the four most relevant features.,10023
BankNoteAuthentication_decision_tree.png,It is clear that variable curtosis is one of the four most relevant features.,10024
BankNoteAuthentication_decision_tree.png,It is clear that variable entropy is one of the four most relevant features.,10025
BankNoteAuthentication_decision_tree.png,It is clear that variable variance is one of the five most relevant features.,10026
BankNoteAuthentication_decision_tree.png,It is clear that variable skewness is one of the five most relevant features.,10027
BankNoteAuthentication_decision_tree.png,It is clear that variable curtosis is one of the five most relevant features.,10028
BankNoteAuthentication_decision_tree.png,It is clear that variable entropy is one of the five most relevant features.,10029
BankNoteAuthentication_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",10030
BankNoteAuthentication_correlation_heatmap.png,Variables skewness and variance are redundant.,10031
BankNoteAuthentication_correlation_heatmap.png,Variables curtosis and variance are redundant.,10032
BankNoteAuthentication_correlation_heatmap.png,Variables entropy and variance are redundant.,10033
BankNoteAuthentication_correlation_heatmap.png,Variables variance and skewness are redundant.,10034
BankNoteAuthentication_correlation_heatmap.png,Variables curtosis and skewness are redundant.,10035
BankNoteAuthentication_correlation_heatmap.png,Variables entropy and skewness are redundant.,10036
BankNoteAuthentication_correlation_heatmap.png,Variables variance and curtosis are redundant.,10037
BankNoteAuthentication_correlation_heatmap.png,Variables skewness and curtosis are redundant.,10038
BankNoteAuthentication_correlation_heatmap.png,Variables entropy and curtosis are redundant.,10039
BankNoteAuthentication_correlation_heatmap.png,Variables variance and entropy are redundant.,10040
BankNoteAuthentication_correlation_heatmap.png,Variables skewness and entropy are redundant.,10041
BankNoteAuthentication_correlation_heatmap.png,Variables curtosis and entropy are redundant.,10042
BankNoteAuthentication_correlation_heatmap.png,"Variables skewness and entropy are redundant, but we can’t say the same for the pair variance and curtosis.",10043
BankNoteAuthentication_correlation_heatmap.png,"Variables skewness and curtosis are redundant, but we can’t say the same for the pair variance and entropy.",10044
BankNoteAuthentication_correlation_heatmap.png,"Variables variance and skewness are redundant, but we can’t say the same for the pair curtosis and entropy.",10045
BankNoteAuthentication_correlation_heatmap.png,"Variables curtosis and entropy are redundant, but we can’t say the same for the pair variance and skewness.",10046
BankNoteAuthentication_correlation_heatmap.png,"Variables variance and curtosis are redundant, but we can’t say the same for the pair skewness and entropy.",10047
BankNoteAuthentication_correlation_heatmap.png,"Variables variance and entropy are redundant, but we can’t say the same for the pair skewness and curtosis.",10048
BankNoteAuthentication_correlation_heatmap.png,The variable variance can be discarded without risking losing information.,10049
BankNoteAuthentication_correlation_heatmap.png,The variable skewness can be discarded without risking losing information.,10050
BankNoteAuthentication_correlation_heatmap.png,The variable curtosis can be discarded without risking losing information.,10051
BankNoteAuthentication_correlation_heatmap.png,The variable entropy can be discarded without risking losing information.,10052
BankNoteAuthentication_correlation_heatmap.png,One of the variables skewness or variance can be discarded without losing information.,10053
BankNoteAuthentication_correlation_heatmap.png,One of the variables curtosis or variance can be discarded without losing information.,10054
BankNoteAuthentication_correlation_heatmap.png,One of the variables entropy or variance can be discarded without losing information.,10055
BankNoteAuthentication_correlation_heatmap.png,One of the variables variance or skewness can be discarded without losing information.,10056
BankNoteAuthentication_correlation_heatmap.png,One of the variables curtosis or skewness can be discarded without losing information.,10057
BankNoteAuthentication_correlation_heatmap.png,One of the variables entropy or skewness can be discarded without losing information.,10058
BankNoteAuthentication_correlation_heatmap.png,One of the variables variance or curtosis can be discarded without losing information.,10059
BankNoteAuthentication_correlation_heatmap.png,One of the variables skewness or curtosis can be discarded without losing information.,10060
BankNoteAuthentication_correlation_heatmap.png,One of the variables entropy or curtosis can be discarded without losing information.,10061
BankNoteAuthentication_correlation_heatmap.png,One of the variables variance or entropy can be discarded without losing information.,10062
BankNoteAuthentication_correlation_heatmap.png,One of the variables skewness or entropy can be discarded without losing information.,10063
BankNoteAuthentication_correlation_heatmap.png,One of the variables curtosis or entropy can be discarded without losing information.,10064
BankNoteAuthentication_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,10065
BankNoteAuthentication_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,10066
BankNoteAuthentication_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,10067
BankNoteAuthentication_histograms_numeric.png,At least 50 of the variables present outliers.,10068
BankNoteAuthentication_boxplots.png,At least 60 of the variables present outliers.,10069
BankNoteAuthentication_boxplots.png,At least 75 of the variables present outliers.,10070
BankNoteAuthentication_histograms_numeric.png,At least 85 of the variables present outliers.,10071
BankNoteAuthentication_histograms_numeric.png,Variable variance presents some outliers.,10072
BankNoteAuthentication_histograms_numeric.png,Variable skewness presents some outliers.,10073
BankNoteAuthentication_histograms_numeric.png,Variable curtosis presents some outliers.,10074
BankNoteAuthentication_boxplots.png,Variable entropy presents some outliers.,10075
BankNoteAuthentication_boxplots.png,Variable variance doesn’t have any outliers.,10076
BankNoteAuthentication_boxplots.png,Variable skewness doesn’t have any outliers.,10077
BankNoteAuthentication_boxplots.png,Variable curtosis doesn’t have any outliers.,10078
BankNoteAuthentication_boxplots.png,Variable entropy doesn’t have any outliers.,10079
BankNoteAuthentication_boxplots.png,Variable variance shows some outlier values.,10080
BankNoteAuthentication_histograms_numeric.png,Variable skewness shows some outlier values.,10081
BankNoteAuthentication_boxplots.png,Variable curtosis shows some outlier values.,10082
BankNoteAuthentication_histograms_numeric.png,Variable entropy shows some outlier values.,10083
BankNoteAuthentication_boxplots.png,Variable variance shows a high number of outlier values.,10084
BankNoteAuthentication_histograms_numeric.png,Variable skewness shows a high number of outlier values.,10085
BankNoteAuthentication_boxplots.png,Variable curtosis shows a high number of outlier values.,10086
BankNoteAuthentication_boxplots.png,Variable entropy shows a high number of outlier values.,10087
BankNoteAuthentication_histograms_numeric.png,Outliers seem to be a problem in the dataset.,10088
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable skewness shows some outliers, but we can’t be sure of the same for variable variance.",10089
BankNoteAuthentication_boxplots.png,"It is clear that variable curtosis shows some outliers, but we can’t be sure of the same for variable variance.",10090
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable entropy shows some outliers, but we can’t be sure of the same for variable variance.",10091
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable variance shows some outliers, but we can’t be sure of the same for variable skewness.",10092
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable curtosis shows some outliers, but we can’t be sure of the same for variable skewness.",10093
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable entropy shows some outliers, but we can’t be sure of the same for variable skewness.",10094
BankNoteAuthentication_boxplots.png,"It is clear that variable variance shows some outliers, but we can’t be sure of the same for variable curtosis.",10095
BankNoteAuthentication_boxplots.png,"It is clear that variable skewness shows some outliers, but we can’t be sure of the same for variable curtosis.",10096
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable entropy shows some outliers, but we can’t be sure of the same for variable curtosis.",10097
BankNoteAuthentication_boxplots.png,"It is clear that variable variance shows some outliers, but we can’t be sure of the same for variable entropy.",10098
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable skewness shows some outliers, but we can’t be sure of the same for variable entropy.",10099
BankNoteAuthentication_histograms_numeric.png,"It is clear that variable curtosis shows some outliers, but we can’t be sure of the same for variable entropy.",10100
BankNoteAuthentication_boxplots.png,Those boxplots show that the data is not normalized.,10101
BankNoteAuthentication_boxplots.png,Variable variance is balanced.,10102
BankNoteAuthentication_histograms_numeric.png,Variable skewness is balanced.,10103
BankNoteAuthentication_boxplots.png,Variable curtosis is balanced.,10104
BankNoteAuthentication_histograms_numeric.png,Variable entropy is balanced.,10105
BankNoteAuthentication_histograms.png,The variable variance can be seen as ordinal without losing information.,10106
BankNoteAuthentication_histograms.png,The variable skewness can be seen as ordinal without losing information.,10107
BankNoteAuthentication_histograms.png,The variable curtosis can be seen as ordinal without losing information.,10108
BankNoteAuthentication_histograms.png,The variable entropy can be seen as ordinal without losing information.,10109
BankNoteAuthentication_histograms.png,The variable variance can be seen as ordinal.,10110
BankNoteAuthentication_histograms.png,The variable skewness can be seen as ordinal.,10111
BankNoteAuthentication_histograms.png,The variable curtosis can be seen as ordinal.,10112
BankNoteAuthentication_histograms.png,The variable entropy can be seen as ordinal.,10113
BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as numeric.",10114
BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as binary.",10115
BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as date.",10116
BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as symbolic.",10117
BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 62.,10118
BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 51.,10119
BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 19.,10120
BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 22.,10121
BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 71.,10122
BankNoteAuthentication_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,10123
BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",10124
BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",10125
BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",10126
BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",10127
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",10128
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",10129
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",10130
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",10131
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 0.",10132
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 0.",10133
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 0.",10134
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 0.",10135
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 111.",10136
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 111.",10137
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 111.",10138
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 111.",10139
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 111.",10140
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 111.",10141
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 111.",10142
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 111.",10143
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 98.",10144
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 98.",10145
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 98.",10146
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 98.",10147
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 98.",10148
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 98.",10149
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 98.",10150
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 98.",10151
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 167.",10152
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 167.",10153
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 167.",10154
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 167.",10155
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 167.",10156
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 167.",10157
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 167.",10158
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 167.",10159
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 161.",10160
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 161.",10161
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 161.",10162
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 161.",10163
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 161.",10164
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 161.",10165
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 161.",10166
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 161.",10167
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (A,B) as 1.",10168
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (not A, B) as 1.",10169
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (A, not B) as 1.",10170
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (not A, not B) as 1.",10171
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (A,B) as 0.",10172
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (not A, B) as 0.",10173
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (A, not B) as 0.",10174
diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (not A, not B) as 0.",10175
diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 770 episodes.,10176
diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1084 episodes.,10177
diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1242 episodes.,10178
diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1062 episodes.,10179
diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1369 episodes.,10180
diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 119 estimators.,10181
diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 103 estimators.,10182
diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 91 estimators.,10183
diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 134 estimators.,10184
diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 56 estimators.,10185
diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 72 estimators.,10186
diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 134 estimators.,10187
diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 146 estimators.,10188
diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 56 estimators.,10189
diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 111 estimators.,10190
diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,10191
diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,10192
diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,10193
diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,10194
diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,10195
diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,10196
diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,10197
diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,10198
diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,10199
diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,10200
diabetes_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,10201
diabetes_decision_tree.png,The recall for the presented tree is higher than its accuracy.,10202
diabetes_decision_tree.png,The precision for the presented tree is higher than its accuracy.,10203
diabetes_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,10204
diabetes_decision_tree.png,The recall for the presented tree is lower than its accuracy.,10205
diabetes_decision_tree.png,The precision for the presented tree is lower than its accuracy.,10206
diabetes_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,10207
diabetes_decision_tree.png,The accuracy for the presented tree is higher than its recall.,10208
diabetes_decision_tree.png,The precision for the presented tree is higher than its recall.,10209
diabetes_decision_tree.png,The specificity for the presented tree is higher than its recall.,10210
diabetes_decision_tree.png,The accuracy for the presented tree is lower than its recall.,10211
diabetes_decision_tree.png,The precision for the presented tree is lower than its recall.,10212
diabetes_decision_tree.png,The specificity for the presented tree is lower than its recall.,10213
diabetes_decision_tree.png,The accuracy for the presented tree is higher than its precision.,10214
diabetes_decision_tree.png,The recall for the presented tree is higher than its precision.,10215
diabetes_decision_tree.png,The specificity for the presented tree is higher than its precision.,10216
diabetes_decision_tree.png,The accuracy for the presented tree is lower than its precision.,10217
diabetes_decision_tree.png,The recall for the presented tree is lower than its precision.,10218
diabetes_decision_tree.png,The specificity for the presented tree is lower than its precision.,10219
diabetes_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,10220
diabetes_decision_tree.png,The recall for the presented tree is higher than its specificity.,10221
diabetes_decision_tree.png,The precision for the presented tree is higher than its specificity.,10222
diabetes_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,10223
diabetes_decision_tree.png,The recall for the presented tree is lower than its specificity.,10224
diabetes_decision_tree.png,The precision for the presented tree is lower than its specificity.,10225
diabetes_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,10226
diabetes_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,10227
diabetes_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,10228
diabetes_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,10229
diabetes_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,10230
diabetes_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,10231
diabetes_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,10232
diabetes_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,10233
diabetes_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,10234
diabetes_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,10235
diabetes_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,10236
diabetes_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,10237
diabetes_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,10238
diabetes_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,10239
diabetes_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,10240
diabetes_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,10241
diabetes_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,10242
diabetes_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,10243
diabetes_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,10244
diabetes_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,10245
diabetes_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,10246
diabetes_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,10247
diabetes_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,10248
diabetes_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,10249
diabetes_decision_tree.png,The number of True Positives reported in the same tree is 49.,10250
diabetes_decision_tree.png,The number of False Positives reported in the same tree is 48.,10251
diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 10.,10252
diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 39.,10253
diabetes_decision_tree.png,The number of True Positives reported in the same tree is 31.,10254
diabetes_decision_tree.png,The number of False Positives reported in the same tree is 32.,10255
diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 26.,10256
diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 16.,10257
diabetes_decision_tree.png,The number of True Positives reported in the same tree is 25.,10258
diabetes_decision_tree.png,The number of False Positives reported in the same tree is 23.,10259
diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 15.,10260
diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 42.,10261
diabetes_decision_tree.png,The number of True Positives reported in the same tree is 33.,10262
diabetes_decision_tree.png,The number of False Positives reported in the same tree is 41.,10263
diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 37.,10264
diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 28.,10265
diabetes_decision_tree.png,The number of True Positives reported in the same tree is 11.,10266
diabetes_decision_tree.png,The number of False Positives reported in the same tree is 30.,10267
diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 24.,10268
diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 17.,10269
diabetes_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,10270
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,10271
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,10272
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,10273
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,10274
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,10275
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,10276
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,10277
diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,10278
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,10279
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,10280
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,10281
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,10282
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,10283
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,10284
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,10285
diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,10286
diabetes_decision_tree.png,The accuracy for the presented tree is higher than 70%.,10287
diabetes_decision_tree.png,The recall for the presented tree is higher than 83%.,10288
diabetes_decision_tree.png,The precision for the presented tree is higher than 73%.,10289
diabetes_decision_tree.png,The specificity for the presented tree is higher than 85%.,10290
diabetes_decision_tree.png,The accuracy for the presented tree is lower than 61%.,10291
diabetes_decision_tree.png,The recall for the presented tree is lower than 89%.,10292
diabetes_decision_tree.png,The precision for the presented tree is lower than 64%.,10293
diabetes_decision_tree.png,The specificity for the presented tree is lower than 62%.,10294
diabetes_decision_tree.png,The accuracy for the presented tree is higher than 67%.,10295
diabetes_decision_tree.png,The recall for the presented tree is higher than 69%.,10296
diabetes_decision_tree.png,The precision for the presented tree is higher than 90%.,10297
diabetes_decision_tree.png,The specificity for the presented tree is higher than 68%.,10298
diabetes_decision_tree.png,The accuracy for the presented tree is lower than 78%.,10299
diabetes_decision_tree.png,The recall for the presented tree is lower than 71%.,10300
diabetes_decision_tree.png,The precision for the presented tree is lower than 66%.,10301
diabetes_decision_tree.png,The specificity for the presented tree is lower than 80%.,10302
diabetes_decision_tree.png,The accuracy for the presented tree is higher than 81%.,10303
diabetes_decision_tree.png,The recall for the presented tree is higher than 86%.,10304
diabetes_decision_tree.png,The precision for the presented tree is higher than 77%.,10305
diabetes_decision_tree.png,The specificity for the presented tree is higher than 65%.,10306
diabetes_decision_tree.png,The accuracy for the presented tree is lower than 72%.,10307
diabetes_decision_tree.png,The recall for the presented tree is lower than 82%.,10308
diabetes_decision_tree.png,The precision for the presented tree is lower than 60%.,10309
diabetes_decision_tree.png,The specificity for the presented tree is lower than 63%.,10310
diabetes_decision_tree.png,The accuracy for the presented tree is higher than 75%.,10311
diabetes_decision_tree.png,The recall for the presented tree is higher than 79%.,10312
diabetes_decision_tree.png,The precision for the presented tree is higher than 76%.,10313
diabetes_decision_tree.png,The specificity for the presented tree is higher than 88%.,10314
diabetes_decision_tree.png,The accuracy for the presented tree is lower than 74%.,10315
diabetes_decision_tree.png,The recall for the presented tree is lower than 84%.,10316
diabetes_decision_tree.png,The precision for the presented tree is lower than 87%.,10317
diabetes_decision_tree.png,The specificity for the presented tree is lower than 74%.,10318
diabetes_decision_tree.png,The accuracy for the presented tree is higher than 86%.,10319
diabetes_decision_tree.png,The recall for the presented tree is higher than 75%.,10320
diabetes_decision_tree.png,The precision for the presented tree is higher than 70%.,10321
diabetes_decision_tree.png,The specificity for the presented tree is higher than 68%.,10322
diabetes_decision_tree.png,The accuracy for the presented tree is lower than 75%.,10323
diabetes_decision_tree.png,The recall for the presented tree is lower than 73%.,10324
diabetes_decision_tree.png,The precision for the presented tree is lower than 68%.,10325
diabetes_decision_tree.png,The specificity for the presented tree is lower than 87%.,10326
diabetes_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",10327
diabetes_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",10328
diabetes_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",10329
diabetes_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",10330
diabetes_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",10331
diabetes_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",10332
diabetes_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,10333
diabetes_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,10334
diabetes_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,10335
diabetes_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,10336
diabetes_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,10337
diabetes_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,10338
diabetes_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,10339
diabetes_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,10340
diabetes_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,10341
diabetes_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,10342
diabetes_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,10343
diabetes_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,10344
diabetes_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,10345
diabetes_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,10346
diabetes_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,10347
diabetes_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,10348
diabetes_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,10349
diabetes_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,10350
diabetes_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,10351
diabetes_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,10352
diabetes_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,10353
diabetes_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,10354
diabetes_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,10355
diabetes_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,10356
diabetes_overfitting_knn.png,KNN is in overfitting for k less than 2.,10357
diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 2.,10358
diabetes_overfitting_knn.png,KNN is in overfitting for k less than 3.,10359
diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 3.,10360
diabetes_overfitting_knn.png,KNN is in overfitting for k less than 4.,10361
diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 4.,10362
diabetes_overfitting_knn.png,KNN is in overfitting for k less than 5.,10363
diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 5.,10364
diabetes_overfitting_knn.png,KNN is in overfitting for k less than 6.,10365
diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 6.,10366
diabetes_overfitting_knn.png,KNN is in overfitting for k less than 7.,10367
diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 7.,10368
diabetes_overfitting_knn.png,KNN is in overfitting for k less than 8.,10369
diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 8.,10370
diabetes_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",10371
diabetes_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",10372
diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",10373
diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",10374
diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",10375
diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",10376
diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",10377
diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",10378
diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",10379
diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",10380
diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",10381
diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",10382
diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",10383
diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",10384
diabetes_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,10385
diabetes_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,10386
diabetes_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,10387
diabetes_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,10388
diabetes_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,10389
diabetes_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,10390
diabetes_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,10391
diabetes_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,10392
diabetes_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,10393
diabetes_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,10394
diabetes_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,10395
diabetes_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,10396
diabetes_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,10397
diabetes_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,10398
diabetes_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,10399
diabetes_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,10400
diabetes_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,10401
diabetes_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,10402
diabetes_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,10403
diabetes_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,10404
diabetes_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,10405
diabetes_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,10406
diabetes_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,10407
diabetes_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,10408
diabetes_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,10409
diabetes_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,10410
diabetes_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,10411
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable Pregnancies.,10412
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable Pregnancies.,10413
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable Pregnancies.,10414
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Insulin previously than variable Pregnancies.,10415
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable Pregnancies.,10416
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable Pregnancies.,10417
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Pregnancies.,10418
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable Glucose.,10419
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable Glucose.,10420
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable Glucose.,10421
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Insulin previously than variable Glucose.,10422
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable Glucose.,10423
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable Glucose.,10424
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Glucose.,10425
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable BloodPressure.,10426
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable BloodPressure.,10427
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable BloodPressure.,10428
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Insulin previously than variable BloodPressure.,10429
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable BloodPressure.,10430
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable BloodPressure.,10431
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable BloodPressure.,10432
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable SkinThickness.,10433
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable SkinThickness.,10434
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable SkinThickness.,10435
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Insulin previously than variable SkinThickness.,10436
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable SkinThickness.,10437
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable SkinThickness.,10438
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable SkinThickness.,10439
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable Insulin.,10440
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable Insulin.,10441
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable Insulin.,10442
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable Insulin.,10443
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable Insulin.,10444
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable Insulin.,10445
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Insulin.,10446
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable BMI.,10447
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable BMI.,10448
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable BMI.,10449
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable BMI.,10450
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Insulin previously than variable BMI.,10451
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable BMI.,10452
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable BMI.,10453
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable DiabetesPedigreeFunction.,10454
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable DiabetesPedigreeFunction.,10455
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable DiabetesPedigreeFunction.,10456
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable DiabetesPedigreeFunction.,10457
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Insulin previously than variable DiabetesPedigreeFunction.,10458
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable DiabetesPedigreeFunction.,10459
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable DiabetesPedigreeFunction.,10460
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable Age.,10461
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable Age.,10462
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable Age.,10463
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable Age.,10464
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Insulin previously than variable Age.,10465
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable Age.,10466
diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable Age.,10467
diabetes_pca.png,The first 2 principal components are enough for explaining half the data variance.,10468
diabetes_pca.png,The first 3 principal components are enough for explaining half the data variance.,10469
diabetes_pca.png,The first 4 principal components are enough for explaining half the data variance.,10470
diabetes_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,10471
diabetes_correlation_heatmap.png,Removing variable Pregnancies might improve the training of decision trees .,10472
diabetes_correlation_heatmap.png,Removing variable Glucose might improve the training of decision trees .,10473
diabetes_correlation_heatmap.png,Removing variable BloodPressure might improve the training of decision trees .,10474
diabetes_correlation_heatmap.png,Removing variable SkinThickness might improve the training of decision trees .,10475
diabetes_correlation_heatmap.png,Removing variable Insulin might improve the training of decision trees .,10476
diabetes_correlation_heatmap.png,Removing variable BMI might improve the training of decision trees .,10477
diabetes_correlation_heatmap.png,Removing variable DiabetesPedigreeFunction might improve the training of decision trees .,10478
diabetes_correlation_heatmap.png,Removing variable Age might improve the training of decision trees .,10479
diabetes_histograms.png,"Not knowing the semantics of Pregnancies variable, dummification could have been a more adequate codification.",10480
diabetes_histograms.png,"Not knowing the semantics of Glucose variable, dummification could have been a more adequate codification.",10481
diabetes_histograms.png,"Not knowing the semantics of BloodPressure variable, dummification could have been a more adequate codification.",10482
diabetes_histograms.png,"Not knowing the semantics of SkinThickness variable, dummification could have been a more adequate codification.",10483
diabetes_histograms.png,"Not knowing the semantics of Insulin variable, dummification could have been a more adequate codification.",10484
diabetes_histograms.png,"Not knowing the semantics of BMI variable, dummification could have been a more adequate codification.",10485
diabetes_histograms.png,"Not knowing the semantics of DiabetesPedigreeFunction variable, dummification could have been a more adequate codification.",10486
diabetes_histograms.png,"Not knowing the semantics of Age variable, dummification could have been a more adequate codification.",10487
diabetes_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,10488
diabetes_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",10489
diabetes_histograms.png,"Given the usual semantics of Pregnancies variable, dummification would have been a better codification.",10490
diabetes_histograms.png,"Given the usual semantics of Glucose variable, dummification would have been a better codification.",10491
diabetes_histograms.png,"Given the usual semantics of BloodPressure variable, dummification would have been a better codification.",10492
diabetes_histograms.png,"Given the usual semantics of SkinThickness variable, dummification would have been a better codification.",10493
diabetes_histograms.png,"Given the usual semantics of Insulin variable, dummification would have been a better codification.",10494
diabetes_histograms.png,"Given the usual semantics of BMI variable, dummification would have been a better codification.",10495
diabetes_histograms.png,"Given the usual semantics of DiabetesPedigreeFunction variable, dummification would have been a better codification.",10496
diabetes_histograms.png,"Given the usual semantics of Age variable, dummification would have been a better codification.",10497
diabetes_histograms.png,The variable Pregnancies can be coded as ordinal without losing information.,10498
diabetes_histograms.png,The variable Glucose can be coded as ordinal without losing information.,10499
diabetes_histograms.png,The variable BloodPressure can be coded as ordinal without losing information.,10500
diabetes_histograms.png,The variable SkinThickness can be coded as ordinal without losing information.,10501
diabetes_histograms.png,The variable Insulin can be coded as ordinal without losing information.,10502
diabetes_histograms.png,The variable BMI can be coded as ordinal without losing information.,10503
diabetes_histograms.png,The variable DiabetesPedigreeFunction can be coded as ordinal without losing information.,10504
diabetes_histograms.png,The variable Age can be coded as ordinal without losing information.,10505
diabetes_histograms.png,"Considering the common semantics for Pregnancies variable, dummification would be the most adequate encoding.",10506
diabetes_histograms.png,"Considering the common semantics for Glucose variable, dummification would be the most adequate encoding.",10507
diabetes_histograms.png,"Considering the common semantics for BloodPressure variable, dummification would be the most adequate encoding.",10508
diabetes_histograms.png,"Considering the common semantics for SkinThickness variable, dummification would be the most adequate encoding.",10509
diabetes_histograms.png,"Considering the common semantics for Insulin variable, dummification would be the most adequate encoding.",10510
diabetes_histograms.png,"Considering the common semantics for BMI variable, dummification would be the most adequate encoding.",10511
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction variable, dummification would be the most adequate encoding.",10512
diabetes_histograms.png,"Considering the common semantics for Age variable, dummification would be the most adequate encoding.",10513
diabetes_histograms.png,"Considering the common semantics for Glucose and Pregnancies variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10514
diabetes_histograms.png,"Considering the common semantics for BloodPressure and Pregnancies variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10515
diabetes_histograms.png,"Considering the common semantics for SkinThickness and Pregnancies variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10516
diabetes_histograms.png,"Considering the common semantics for Insulin and Pregnancies variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10517
diabetes_histograms.png,"Considering the common semantics for BMI and Pregnancies variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10518
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction and Pregnancies variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10519
diabetes_histograms.png,"Considering the common semantics for Age and Pregnancies variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10520
diabetes_histograms.png,"Considering the common semantics for Pregnancies and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10521
diabetes_histograms.png,"Considering the common semantics for BloodPressure and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10522
diabetes_histograms.png,"Considering the common semantics for SkinThickness and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10523
diabetes_histograms.png,"Considering the common semantics for Insulin and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10524
diabetes_histograms.png,"Considering the common semantics for BMI and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10525
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10526
diabetes_histograms.png,"Considering the common semantics for Age and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10527
diabetes_histograms.png,"Considering the common semantics for Pregnancies and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10528
diabetes_histograms.png,"Considering the common semantics for Glucose and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10529
diabetes_histograms.png,"Considering the common semantics for SkinThickness and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10530
diabetes_histograms.png,"Considering the common semantics for Insulin and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10531
diabetes_histograms.png,"Considering the common semantics for BMI and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10532
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10533
diabetes_histograms.png,"Considering the common semantics for Age and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10534
diabetes_histograms.png,"Considering the common semantics for Pregnancies and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10535
diabetes_histograms.png,"Considering the common semantics for Glucose and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10536
diabetes_histograms.png,"Considering the common semantics for BloodPressure and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10537
diabetes_histograms.png,"Considering the common semantics for Insulin and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10538
diabetes_histograms.png,"Considering the common semantics for BMI and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10539
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10540
diabetes_histograms.png,"Considering the common semantics for Age and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10541
diabetes_histograms.png,"Considering the common semantics for Pregnancies and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10542
diabetes_histograms.png,"Considering the common semantics for Glucose and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10543
diabetes_histograms.png,"Considering the common semantics for BloodPressure and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10544
diabetes_histograms.png,"Considering the common semantics for SkinThickness and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10545
diabetes_histograms.png,"Considering the common semantics for BMI and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10546
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10547
diabetes_histograms.png,"Considering the common semantics for Age and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10548
diabetes_histograms.png,"Considering the common semantics for Pregnancies and BMI variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10549
diabetes_histograms.png,"Considering the common semantics for Glucose and BMI variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10550
diabetes_histograms.png,"Considering the common semantics for BloodPressure and BMI variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10551
diabetes_histograms.png,"Considering the common semantics for SkinThickness and BMI variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10552
diabetes_histograms.png,"Considering the common semantics for Insulin and BMI variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10553
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction and BMI variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10554
diabetes_histograms.png,"Considering the common semantics for Age and BMI variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10555
diabetes_histograms.png,"Considering the common semantics for Pregnancies and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10556
diabetes_histograms.png,"Considering the common semantics for Glucose and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10557
diabetes_histograms.png,"Considering the common semantics for BloodPressure and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10558
diabetes_histograms.png,"Considering the common semantics for SkinThickness and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10559
diabetes_histograms.png,"Considering the common semantics for Insulin and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10560
diabetes_histograms.png,"Considering the common semantics for BMI and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10561
diabetes_histograms.png,"Considering the common semantics for Age and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10562
diabetes_histograms.png,"Considering the common semantics for Pregnancies and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10563
diabetes_histograms.png,"Considering the common semantics for Glucose and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10564
diabetes_histograms.png,"Considering the common semantics for BloodPressure and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10565
diabetes_histograms.png,"Considering the common semantics for SkinThickness and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10566
diabetes_histograms.png,"Considering the common semantics for Insulin and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10567
diabetes_histograms.png,"Considering the common semantics for BMI and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10568
diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10569
diabetes_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,10570
diabetes_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,10571
diabetes_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,10572
diabetes_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",10573
diabetes_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",10574
diabetes_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",10575
diabetes_correlation_heatmap.png,Variables Glucose and Pregnancies seem to be useful for classification tasks.,10576
diabetes_correlation_heatmap.png,Variables BloodPressure and Pregnancies seem to be useful for classification tasks.,10577
diabetes_correlation_heatmap.png,Variables SkinThickness and Pregnancies seem to be useful for classification tasks.,10578
diabetes_correlation_heatmap.png,Variables Insulin and Pregnancies seem to be useful for classification tasks.,10579
diabetes_correlation_heatmap.png,Variables BMI and Pregnancies seem to be useful for classification tasks.,10580
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Pregnancies seem to be useful for classification tasks.,10581
diabetes_correlation_heatmap.png,Variables Age and Pregnancies seem to be useful for classification tasks.,10582
diabetes_correlation_heatmap.png,Variables Pregnancies and Glucose seem to be useful for classification tasks.,10583
diabetes_correlation_heatmap.png,Variables BloodPressure and Glucose seem to be useful for classification tasks.,10584
diabetes_correlation_heatmap.png,Variables SkinThickness and Glucose seem to be useful for classification tasks.,10585
diabetes_correlation_heatmap.png,Variables Insulin and Glucose seem to be useful for classification tasks.,10586
diabetes_correlation_heatmap.png,Variables BMI and Glucose seem to be useful for classification tasks.,10587
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Glucose seem to be useful for classification tasks.,10588
diabetes_correlation_heatmap.png,Variables Age and Glucose seem to be useful for classification tasks.,10589
diabetes_correlation_heatmap.png,Variables Pregnancies and BloodPressure seem to be useful for classification tasks.,10590
diabetes_correlation_heatmap.png,Variables Glucose and BloodPressure seem to be useful for classification tasks.,10591
diabetes_correlation_heatmap.png,Variables SkinThickness and BloodPressure seem to be useful for classification tasks.,10592
diabetes_correlation_heatmap.png,Variables Insulin and BloodPressure seem to be useful for classification tasks.,10593
diabetes_correlation_heatmap.png,Variables BMI and BloodPressure seem to be useful for classification tasks.,10594
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and BloodPressure seem to be useful for classification tasks.,10595
diabetes_correlation_heatmap.png,Variables Age and BloodPressure seem to be useful for classification tasks.,10596
diabetes_correlation_heatmap.png,Variables Pregnancies and SkinThickness seem to be useful for classification tasks.,10597
diabetes_correlation_heatmap.png,Variables Glucose and SkinThickness seem to be useful for classification tasks.,10598
diabetes_correlation_heatmap.png,Variables BloodPressure and SkinThickness seem to be useful for classification tasks.,10599
diabetes_correlation_heatmap.png,Variables Insulin and SkinThickness seem to be useful for classification tasks.,10600
diabetes_correlation_heatmap.png,Variables BMI and SkinThickness seem to be useful for classification tasks.,10601
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and SkinThickness seem to be useful for classification tasks.,10602
diabetes_correlation_heatmap.png,Variables Age and SkinThickness seem to be useful for classification tasks.,10603
diabetes_correlation_heatmap.png,Variables Pregnancies and Insulin seem to be useful for classification tasks.,10604
diabetes_correlation_heatmap.png,Variables Glucose and Insulin seem to be useful for classification tasks.,10605
diabetes_correlation_heatmap.png,Variables BloodPressure and Insulin seem to be useful for classification tasks.,10606
diabetes_correlation_heatmap.png,Variables SkinThickness and Insulin seem to be useful for classification tasks.,10607
diabetes_correlation_heatmap.png,Variables BMI and Insulin seem to be useful for classification tasks.,10608
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Insulin seem to be useful for classification tasks.,10609
diabetes_correlation_heatmap.png,Variables Age and Insulin seem to be useful for classification tasks.,10610
diabetes_correlation_heatmap.png,Variables Pregnancies and BMI seem to be useful for classification tasks.,10611
diabetes_correlation_heatmap.png,Variables Glucose and BMI seem to be useful for classification tasks.,10612
diabetes_correlation_heatmap.png,Variables BloodPressure and BMI seem to be useful for classification tasks.,10613
diabetes_correlation_heatmap.png,Variables SkinThickness and BMI seem to be useful for classification tasks.,10614
diabetes_correlation_heatmap.png,Variables Insulin and BMI seem to be useful for classification tasks.,10615
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and BMI seem to be useful for classification tasks.,10616
diabetes_correlation_heatmap.png,Variables Age and BMI seem to be useful for classification tasks.,10617
diabetes_correlation_heatmap.png,Variables Pregnancies and DiabetesPedigreeFunction seem to be useful for classification tasks.,10618
diabetes_correlation_heatmap.png,Variables Glucose and DiabetesPedigreeFunction seem to be useful for classification tasks.,10619
diabetes_correlation_heatmap.png,Variables BloodPressure and DiabetesPedigreeFunction seem to be useful for classification tasks.,10620
diabetes_correlation_heatmap.png,Variables SkinThickness and DiabetesPedigreeFunction seem to be useful for classification tasks.,10621
diabetes_correlation_heatmap.png,Variables Insulin and DiabetesPedigreeFunction seem to be useful for classification tasks.,10622
diabetes_correlation_heatmap.png,Variables BMI and DiabetesPedigreeFunction seem to be useful for classification tasks.,10623
diabetes_correlation_heatmap.png,Variables Age and DiabetesPedigreeFunction seem to be useful for classification tasks.,10624
diabetes_correlation_heatmap.png,Variables Pregnancies and Age seem to be useful for classification tasks.,10625
diabetes_correlation_heatmap.png,Variables Glucose and Age seem to be useful for classification tasks.,10626
diabetes_correlation_heatmap.png,Variables BloodPressure and Age seem to be useful for classification tasks.,10627
diabetes_correlation_heatmap.png,Variables SkinThickness and Age seem to be useful for classification tasks.,10628
diabetes_correlation_heatmap.png,Variables Insulin and Age seem to be useful for classification tasks.,10629
diabetes_correlation_heatmap.png,Variables BMI and Age seem to be useful for classification tasks.,10630
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Age seem to be useful for classification tasks.,10631
diabetes_correlation_heatmap.png,Variable Pregnancies seems to be relevant for the majority of mining tasks.,10632
diabetes_correlation_heatmap.png,Variable Glucose seems to be relevant for the majority of mining tasks.,10633
diabetes_correlation_heatmap.png,Variable BloodPressure seems to be relevant for the majority of mining tasks.,10634
diabetes_correlation_heatmap.png,Variable SkinThickness seems to be relevant for the majority of mining tasks.,10635
diabetes_correlation_heatmap.png,Variable Insulin seems to be relevant for the majority of mining tasks.,10636
diabetes_correlation_heatmap.png,Variable BMI seems to be relevant for the majority of mining tasks.,10637
diabetes_correlation_heatmap.png,Variable DiabetesPedigreeFunction seems to be relevant for the majority of mining tasks.,10638
diabetes_correlation_heatmap.png,Variable Age seems to be relevant for the majority of mining tasks.,10639
diabetes_decision_tree.png,Variable Pregnancies is one of the most relevant variables.,10640
diabetes_decision_tree.png,Variable Glucose is one of the most relevant variables.,10641
diabetes_decision_tree.png,Variable BloodPressure is one of the most relevant variables.,10642
diabetes_decision_tree.png,Variable SkinThickness is one of the most relevant variables.,10643
diabetes_decision_tree.png,Variable Insulin is one of the most relevant variables.,10644
diabetes_decision_tree.png,Variable BMI is one of the most relevant variables.,10645
diabetes_decision_tree.png,Variable DiabetesPedigreeFunction is one of the most relevant variables.,10646
diabetes_decision_tree.png,Variable Age is one of the most relevant variables.,10647
diabetes_decision_tree.png,It is possible to state that Pregnancies is the first most discriminative variable regarding the class.,10648
diabetes_decision_tree.png,It is possible to state that Glucose is the first most discriminative variable regarding the class.,10649
diabetes_decision_tree.png,It is possible to state that BloodPressure is the first most discriminative variable regarding the class.,10650
diabetes_decision_tree.png,It is possible to state that SkinThickness is the first most discriminative variable regarding the class.,10651
diabetes_decision_tree.png,It is possible to state that Insulin is the first most discriminative variable regarding the class.,10652
diabetes_decision_tree.png,It is possible to state that BMI is the first most discriminative variable regarding the class.,10653
diabetes_decision_tree.png,It is possible to state that DiabetesPedigreeFunction is the first most discriminative variable regarding the class.,10654
diabetes_decision_tree.png,It is possible to state that Age is the first most discriminative variable regarding the class.,10655
diabetes_decision_tree.png,It is possible to state that Pregnancies is the second most discriminative variable regarding the class.,10656
diabetes_decision_tree.png,It is possible to state that Glucose is the second most discriminative variable regarding the class.,10657
diabetes_decision_tree.png,It is possible to state that BloodPressure is the second most discriminative variable regarding the class.,10658
diabetes_decision_tree.png,It is possible to state that SkinThickness is the second most discriminative variable regarding the class.,10659
diabetes_decision_tree.png,It is possible to state that Insulin is the second most discriminative variable regarding the class.,10660
diabetes_decision_tree.png,It is possible to state that BMI is the second most discriminative variable regarding the class.,10661
diabetes_decision_tree.png,It is possible to state that DiabetesPedigreeFunction is the second most discriminative variable regarding the class.,10662
diabetes_decision_tree.png,It is possible to state that Age is the second most discriminative variable regarding the class.,10663
diabetes_decision_tree.png,"The variable Pregnancies discriminates between the target values, as shown in the decision tree.",10664
diabetes_decision_tree.png,"The variable Glucose discriminates between the target values, as shown in the decision tree.",10665
diabetes_decision_tree.png,"The variable BloodPressure discriminates between the target values, as shown in the decision tree.",10666
diabetes_decision_tree.png,"The variable SkinThickness discriminates between the target values, as shown in the decision tree.",10667
diabetes_decision_tree.png,"The variable Insulin discriminates between the target values, as shown in the decision tree.",10668
diabetes_decision_tree.png,"The variable BMI discriminates between the target values, as shown in the decision tree.",10669
diabetes_decision_tree.png,"The variable DiabetesPedigreeFunction discriminates between the target values, as shown in the decision tree.",10670
diabetes_decision_tree.png,"The variable Age discriminates between the target values, as shown in the decision tree.",10671
diabetes_decision_tree.png,The variable Pregnancies seems to be one of the two most relevant features.,10672
diabetes_decision_tree.png,The variable Glucose seems to be one of the two most relevant features.,10673
diabetes_decision_tree.png,The variable BloodPressure seems to be one of the two most relevant features.,10674
diabetes_decision_tree.png,The variable SkinThickness seems to be one of the two most relevant features.,10675
diabetes_decision_tree.png,The variable Insulin seems to be one of the two most relevant features.,10676
diabetes_decision_tree.png,The variable BMI seems to be one of the two most relevant features.,10677
diabetes_decision_tree.png,The variable DiabetesPedigreeFunction seems to be one of the two most relevant features.,10678
diabetes_decision_tree.png,The variable Age seems to be one of the two most relevant features.,10679
diabetes_decision_tree.png,The variable Pregnancies seems to be one of the three most relevant features.,10680
diabetes_decision_tree.png,The variable Glucose seems to be one of the three most relevant features.,10681
diabetes_decision_tree.png,The variable BloodPressure seems to be one of the three most relevant features.,10682
diabetes_decision_tree.png,The variable SkinThickness seems to be one of the three most relevant features.,10683
diabetes_decision_tree.png,The variable Insulin seems to be one of the three most relevant features.,10684
diabetes_decision_tree.png,The variable BMI seems to be one of the three most relevant features.,10685
diabetes_decision_tree.png,The variable DiabetesPedigreeFunction seems to be one of the three most relevant features.,10686
diabetes_decision_tree.png,The variable Age seems to be one of the three most relevant features.,10687
diabetes_decision_tree.png,The variable Pregnancies seems to be one of the four most relevant features.,10688
diabetes_decision_tree.png,The variable Glucose seems to be one of the four most relevant features.,10689
diabetes_decision_tree.png,The variable BloodPressure seems to be one of the four most relevant features.,10690
diabetes_decision_tree.png,The variable SkinThickness seems to be one of the four most relevant features.,10691
diabetes_decision_tree.png,The variable Insulin seems to be one of the four most relevant features.,10692
diabetes_decision_tree.png,The variable BMI seems to be one of the four most relevant features.,10693
diabetes_decision_tree.png,The variable DiabetesPedigreeFunction seems to be one of the four most relevant features.,10694
diabetes_decision_tree.png,The variable Age seems to be one of the four most relevant features.,10695
diabetes_decision_tree.png,The variable Pregnancies seems to be one of the five most relevant features.,10696
diabetes_decision_tree.png,The variable Glucose seems to be one of the five most relevant features.,10697
diabetes_decision_tree.png,The variable BloodPressure seems to be one of the five most relevant features.,10698
diabetes_decision_tree.png,The variable SkinThickness seems to be one of the five most relevant features.,10699
diabetes_decision_tree.png,The variable Insulin seems to be one of the five most relevant features.,10700
diabetes_decision_tree.png,The variable BMI seems to be one of the five most relevant features.,10701
diabetes_decision_tree.png,The variable DiabetesPedigreeFunction seems to be one of the five most relevant features.,10702
diabetes_decision_tree.png,The variable Age seems to be one of the five most relevant features.,10703
diabetes_decision_tree.png,It is clear that variable Pregnancies is one of the two most relevant features.,10704
diabetes_decision_tree.png,It is clear that variable Glucose is one of the two most relevant features.,10705
diabetes_decision_tree.png,It is clear that variable BloodPressure is one of the two most relevant features.,10706
diabetes_decision_tree.png,It is clear that variable SkinThickness is one of the two most relevant features.,10707
diabetes_decision_tree.png,It is clear that variable Insulin is one of the two most relevant features.,10708
diabetes_decision_tree.png,It is clear that variable BMI is one of the two most relevant features.,10709
diabetes_decision_tree.png,It is clear that variable DiabetesPedigreeFunction is one of the two most relevant features.,10710
diabetes_decision_tree.png,It is clear that variable Age is one of the two most relevant features.,10711
diabetes_decision_tree.png,It is clear that variable Pregnancies is one of the three most relevant features.,10712
diabetes_decision_tree.png,It is clear that variable Glucose is one of the three most relevant features.,10713
diabetes_decision_tree.png,It is clear that variable BloodPressure is one of the three most relevant features.,10714
diabetes_decision_tree.png,It is clear that variable SkinThickness is one of the three most relevant features.,10715
diabetes_decision_tree.png,It is clear that variable Insulin is one of the three most relevant features.,10716
diabetes_decision_tree.png,It is clear that variable BMI is one of the three most relevant features.,10717
diabetes_decision_tree.png,It is clear that variable DiabetesPedigreeFunction is one of the three most relevant features.,10718
diabetes_decision_tree.png,It is clear that variable Age is one of the three most relevant features.,10719
diabetes_decision_tree.png,It is clear that variable Pregnancies is one of the four most relevant features.,10720
diabetes_decision_tree.png,It is clear that variable Glucose is one of the four most relevant features.,10721
diabetes_decision_tree.png,It is clear that variable BloodPressure is one of the four most relevant features.,10722
diabetes_decision_tree.png,It is clear that variable SkinThickness is one of the four most relevant features.,10723
diabetes_decision_tree.png,It is clear that variable Insulin is one of the four most relevant features.,10724
diabetes_decision_tree.png,It is clear that variable BMI is one of the four most relevant features.,10725
diabetes_decision_tree.png,It is clear that variable DiabetesPedigreeFunction is one of the four most relevant features.,10726
diabetes_decision_tree.png,It is clear that variable Age is one of the four most relevant features.,10727
diabetes_decision_tree.png,It is clear that variable Pregnancies is one of the five most relevant features.,10728
diabetes_decision_tree.png,It is clear that variable Glucose is one of the five most relevant features.,10729
diabetes_decision_tree.png,It is clear that variable BloodPressure is one of the five most relevant features.,10730
diabetes_decision_tree.png,It is clear that variable SkinThickness is one of the five most relevant features.,10731
diabetes_decision_tree.png,It is clear that variable Insulin is one of the five most relevant features.,10732
diabetes_decision_tree.png,It is clear that variable BMI is one of the five most relevant features.,10733
diabetes_decision_tree.png,It is clear that variable DiabetesPedigreeFunction is one of the five most relevant features.,10734
diabetes_decision_tree.png,It is clear that variable Age is one of the five most relevant features.,10735
diabetes_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",10736
diabetes_correlation_heatmap.png,Variables Glucose and Pregnancies are redundant.,10737
diabetes_correlation_heatmap.png,Variables BloodPressure and Pregnancies are redundant.,10738
diabetes_correlation_heatmap.png,Variables SkinThickness and Pregnancies are redundant.,10739
diabetes_correlation_heatmap.png,Variables Insulin and Pregnancies are redundant.,10740
diabetes_correlation_heatmap.png,Variables BMI and Pregnancies are redundant.,10741
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Pregnancies are redundant.,10742
diabetes_correlation_heatmap.png,Variables Age and Pregnancies are redundant.,10743
diabetes_correlation_heatmap.png,Variables Pregnancies and Glucose are redundant.,10744
diabetes_correlation_heatmap.png,Variables BloodPressure and Glucose are redundant.,10745
diabetes_correlation_heatmap.png,Variables SkinThickness and Glucose are redundant.,10746
diabetes_correlation_heatmap.png,Variables Insulin and Glucose are redundant.,10747
diabetes_correlation_heatmap.png,Variables BMI and Glucose are redundant.,10748
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Glucose are redundant.,10749
diabetes_correlation_heatmap.png,Variables Age and Glucose are redundant.,10750
diabetes_correlation_heatmap.png,Variables Pregnancies and BloodPressure are redundant.,10751
diabetes_correlation_heatmap.png,Variables Glucose and BloodPressure are redundant.,10752
diabetes_correlation_heatmap.png,Variables SkinThickness and BloodPressure are redundant.,10753
diabetes_correlation_heatmap.png,Variables Insulin and BloodPressure are redundant.,10754
diabetes_correlation_heatmap.png,Variables BMI and BloodPressure are redundant.,10755
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and BloodPressure are redundant.,10756
diabetes_correlation_heatmap.png,Variables Age and BloodPressure are redundant.,10757
diabetes_correlation_heatmap.png,Variables Pregnancies and SkinThickness are redundant.,10758
diabetes_correlation_heatmap.png,Variables Glucose and SkinThickness are redundant.,10759
diabetes_correlation_heatmap.png,Variables BloodPressure and SkinThickness are redundant.,10760
diabetes_correlation_heatmap.png,Variables Insulin and SkinThickness are redundant.,10761
diabetes_correlation_heatmap.png,Variables BMI and SkinThickness are redundant.,10762
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and SkinThickness are redundant.,10763
diabetes_correlation_heatmap.png,Variables Age and SkinThickness are redundant.,10764
diabetes_correlation_heatmap.png,Variables Pregnancies and Insulin are redundant.,10765
diabetes_correlation_heatmap.png,Variables Glucose and Insulin are redundant.,10766
diabetes_correlation_heatmap.png,Variables BloodPressure and Insulin are redundant.,10767
diabetes_correlation_heatmap.png,Variables SkinThickness and Insulin are redundant.,10768
diabetes_correlation_heatmap.png,Variables BMI and Insulin are redundant.,10769
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Insulin are redundant.,10770
diabetes_correlation_heatmap.png,Variables Age and Insulin are redundant.,10771
diabetes_correlation_heatmap.png,Variables Pregnancies and BMI are redundant.,10772
diabetes_correlation_heatmap.png,Variables Glucose and BMI are redundant.,10773
diabetes_correlation_heatmap.png,Variables BloodPressure and BMI are redundant.,10774
diabetes_correlation_heatmap.png,Variables SkinThickness and BMI are redundant.,10775
diabetes_correlation_heatmap.png,Variables Insulin and BMI are redundant.,10776
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and BMI are redundant.,10777
diabetes_correlation_heatmap.png,Variables Age and BMI are redundant.,10778
diabetes_correlation_heatmap.png,Variables Pregnancies and DiabetesPedigreeFunction are redundant.,10779
diabetes_correlation_heatmap.png,Variables Glucose and DiabetesPedigreeFunction are redundant.,10780
diabetes_correlation_heatmap.png,Variables BloodPressure and DiabetesPedigreeFunction are redundant.,10781
diabetes_correlation_heatmap.png,Variables SkinThickness and DiabetesPedigreeFunction are redundant.,10782
diabetes_correlation_heatmap.png,Variables Insulin and DiabetesPedigreeFunction are redundant.,10783
diabetes_correlation_heatmap.png,Variables BMI and DiabetesPedigreeFunction are redundant.,10784
diabetes_correlation_heatmap.png,Variables Age and DiabetesPedigreeFunction are redundant.,10785
diabetes_correlation_heatmap.png,Variables Pregnancies and Age are redundant.,10786
diabetes_correlation_heatmap.png,Variables Glucose and Age are redundant.,10787
diabetes_correlation_heatmap.png,Variables BloodPressure and Age are redundant.,10788
diabetes_correlation_heatmap.png,Variables SkinThickness and Age are redundant.,10789
diabetes_correlation_heatmap.png,Variables Insulin and Age are redundant.,10790
diabetes_correlation_heatmap.png,Variables BMI and Age are redundant.,10791
diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Age are redundant.,10792
diabetes_correlation_heatmap.png,"Variables SkinThickness and BMI are redundant, but we can’t say the same for the pair Pregnancies and BloodPressure.",10793
diabetes_correlation_heatmap.png,"Variables BloodPressure and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Pregnancies and Glucose.",10794
diabetes_correlation_heatmap.png,"Variables Pregnancies and Age are redundant, but we can’t say the same for the pair BloodPressure and SkinThickness.",10795
diabetes_correlation_heatmap.png,"Variables Insulin and BMI are redundant, but we can’t say the same for the pair BloodPressure and DiabetesPedigreeFunction.",10796
diabetes_correlation_heatmap.png,"Variables Pregnancies and SkinThickness are redundant, but we can’t say the same for the pair Glucose and Insulin.",10797
diabetes_correlation_heatmap.png,"Variables BloodPressure and SkinThickness are redundant, but we can’t say the same for the pair Pregnancies and Insulin.",10798
diabetes_correlation_heatmap.png,"Variables BloodPressure and Age are redundant, but we can’t say the same for the pair Pregnancies and SkinThickness.",10799
diabetes_correlation_heatmap.png,"Variables BloodPressure and BMI are redundant, but we can’t say the same for the pair Pregnancies and SkinThickness.",10800
diabetes_correlation_heatmap.png,"Variables Pregnancies and BloodPressure are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10801
diabetes_correlation_heatmap.png,"Variables Pregnancies and SkinThickness are redundant, but we can’t say the same for the pair BMI and DiabetesPedigreeFunction.",10802
diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair Pregnancies and SkinThickness.",10803
diabetes_correlation_heatmap.png,"Variables Insulin and Age are redundant, but we can’t say the same for the pair Glucose and BloodPressure.",10804
diabetes_correlation_heatmap.png,"Variables BloodPressure and Age are redundant, but we can’t say the same for the pair BMI and DiabetesPedigreeFunction.",10805
diabetes_correlation_heatmap.png,"Variables Insulin and Age are redundant, but we can’t say the same for the pair Glucose and DiabetesPedigreeFunction.",10806
diabetes_correlation_heatmap.png,"Variables Glucose and Insulin are redundant, but we can’t say the same for the pair Pregnancies and BMI.",10807
diabetes_correlation_heatmap.png,"Variables Glucose and BloodPressure are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10808
diabetes_correlation_heatmap.png,"Variables Glucose and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and Age.",10809
diabetes_correlation_heatmap.png,"Variables Pregnancies and BMI are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10810
diabetes_correlation_heatmap.png,"Variables Insulin and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10811
diabetes_correlation_heatmap.png,"Variables Pregnancies and SkinThickness are redundant, but we can’t say the same for the pair Glucose and BloodPressure.",10812
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair BloodPressure and Age.",10813
diabetes_correlation_heatmap.png,"Variables BloodPressure and Insulin are redundant, but we can’t say the same for the pair SkinThickness and DiabetesPedigreeFunction.",10814
diabetes_correlation_heatmap.png,"Variables SkinThickness and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and Age.",10815
diabetes_correlation_heatmap.png,"Variables Insulin and Age are redundant, but we can’t say the same for the pair Pregnancies and DiabetesPedigreeFunction.",10816
diabetes_correlation_heatmap.png,"Variables SkinThickness and Insulin are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10817
diabetes_correlation_heatmap.png,"Variables DiabetesPedigreeFunction and Age are redundant, but we can’t say the same for the pair Glucose and SkinThickness.",10818
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair BloodPressure and Insulin.",10819
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Insulin and Age.",10820
diabetes_correlation_heatmap.png,"Variables BMI and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Insulin and Age.",10821
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BMI and Age.",10822
diabetes_correlation_heatmap.png,"Variables SkinThickness and Age are redundant, but we can’t say the same for the pair Glucose and DiabetesPedigreeFunction.",10823
diabetes_correlation_heatmap.png,"Variables Glucose and BloodPressure are redundant, but we can’t say the same for the pair SkinThickness and DiabetesPedigreeFunction.",10824
diabetes_correlation_heatmap.png,"Variables BMI and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and Age.",10825
diabetes_correlation_heatmap.png,"Variables BloodPressure and BMI are redundant, but we can’t say the same for the pair Glucose and DiabetesPedigreeFunction.",10826
diabetes_correlation_heatmap.png,"Variables BloodPressure and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10827
diabetes_correlation_heatmap.png,"Variables BMI and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Glucose and SkinThickness.",10828
diabetes_correlation_heatmap.png,"Variables Pregnancies and Insulin are redundant, but we can’t say the same for the pair Glucose and BloodPressure.",10829
diabetes_correlation_heatmap.png,"Variables Pregnancies and Glucose are redundant, but we can’t say the same for the pair SkinThickness and DiabetesPedigreeFunction.",10830
diabetes_correlation_heatmap.png,"Variables Insulin and Age are redundant, but we can’t say the same for the pair Pregnancies and BloodPressure.",10831
diabetes_correlation_heatmap.png,"Variables Insulin and Age are redundant, but we can’t say the same for the pair Glucose and SkinThickness.",10832
diabetes_correlation_heatmap.png,"Variables BMI and Age are redundant, but we can’t say the same for the pair Pregnancies and SkinThickness.",10833
diabetes_correlation_heatmap.png,"Variables DiabetesPedigreeFunction and Age are redundant, but we can’t say the same for the pair Insulin and BMI.",10834
diabetes_correlation_heatmap.png,"Variables Pregnancies and Insulin are redundant, but we can’t say the same for the pair BloodPressure and Age.",10835
diabetes_correlation_heatmap.png,"Variables Glucose and Insulin are redundant, but we can’t say the same for the pair SkinThickness and Age.",10836
diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair Insulin and DiabetesPedigreeFunction.",10837
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Glucose and BloodPressure.",10838
diabetes_correlation_heatmap.png,"Variables BMI and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Glucose and BloodPressure.",10839
diabetes_correlation_heatmap.png,"Variables DiabetesPedigreeFunction and Age are redundant, but we can’t say the same for the pair SkinThickness and Insulin.",10840
diabetes_correlation_heatmap.png,"Variables Glucose and Insulin are redundant, but we can’t say the same for the pair Pregnancies and SkinThickness.",10841
diabetes_correlation_heatmap.png,"Variables BloodPressure and Insulin are redundant, but we can’t say the same for the pair Pregnancies and BMI.",10842
diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair Pregnancies and DiabetesPedigreeFunction.",10843
diabetes_correlation_heatmap.png,"Variables BloodPressure and Insulin are redundant, but we can’t say the same for the pair SkinThickness and Age.",10844
diabetes_correlation_heatmap.png,"Variables SkinThickness and BMI are redundant, but we can’t say the same for the pair Insulin and DiabetesPedigreeFunction.",10845
diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair BloodPressure and DiabetesPedigreeFunction.",10846
diabetes_correlation_heatmap.png,"Variables SkinThickness and Insulin are redundant, but we can’t say the same for the pair BloodPressure and DiabetesPedigreeFunction.",10847
diabetes_correlation_heatmap.png,"Variables SkinThickness and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and BMI.",10848
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and SkinThickness.",10849
diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair BMI and DiabetesPedigreeFunction.",10850
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Glucose and Age.",10851
diabetes_correlation_heatmap.png,"Variables BMI and Age are redundant, but we can’t say the same for the pair Pregnancies and BloodPressure.",10852
diabetes_correlation_heatmap.png,"Variables SkinThickness and Insulin are redundant, but we can’t say the same for the pair Pregnancies and BMI.",10853
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair SkinThickness and Insulin.",10854
diabetes_correlation_heatmap.png,"Variables SkinThickness and Insulin are redundant, but we can’t say the same for the pair Glucose and Age.",10855
diabetes_correlation_heatmap.png,"Variables BMI and Age are redundant, but we can’t say the same for the pair Pregnancies and Glucose.",10856
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and BMI.",10857
diabetes_correlation_heatmap.png,"Variables BloodPressure and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Pregnancies and Insulin.",10858
diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair Pregnancies and Insulin.",10859
diabetes_correlation_heatmap.png,"Variables BMI and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair SkinThickness and Insulin.",10860
diabetes_correlation_heatmap.png,"Variables Glucose and Insulin are redundant, but we can’t say the same for the pair Pregnancies and BloodPressure.",10861
diabetes_correlation_heatmap.png,"Variables Pregnancies and Insulin are redundant, but we can’t say the same for the pair BloodPressure and SkinThickness.",10862
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10863
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair Pregnancies and Age.",10864
diabetes_correlation_heatmap.png,"Variables BMI and Age are redundant, but we can’t say the same for the pair Glucose and SkinThickness.",10865
diabetes_correlation_heatmap.png,"Variables DiabetesPedigreeFunction and Age are redundant, but we can’t say the same for the pair Pregnancies and BloodPressure.",10866
diabetes_correlation_heatmap.png,"Variables BMI and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair SkinThickness and Age.",10867
diabetes_correlation_heatmap.png,"Variables Pregnancies and SkinThickness are redundant, but we can’t say the same for the pair Insulin and BMI.",10868
diabetes_correlation_heatmap.png,"Variables Pregnancies and BloodPressure are redundant, but we can’t say the same for the pair BMI and Age.",10869
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair BloodPressure and DiabetesPedigreeFunction.",10870
diabetes_correlation_heatmap.png,"Variables SkinThickness and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Insulin and BMI.",10871
diabetes_correlation_heatmap.png,"Variables SkinThickness and Age are redundant, but we can’t say the same for the pair Pregnancies and Glucose.",10872
diabetes_correlation_heatmap.png,"Variables Insulin and BMI are redundant, but we can’t say the same for the pair Pregnancies and SkinThickness.",10873
diabetes_correlation_heatmap.png,"Variables Insulin and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Pregnancies and Glucose.",10874
diabetes_correlation_heatmap.png,"Variables Insulin and Age are redundant, but we can’t say the same for the pair BloodPressure and BMI.",10875
diabetes_correlation_heatmap.png,"Variables SkinThickness and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Pregnancies and Glucose.",10876
diabetes_correlation_heatmap.png,"Variables SkinThickness and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Glucose and Insulin.",10877
diabetes_correlation_heatmap.png,"Variables Glucose and SkinThickness are redundant, but we can’t say the same for the pair BloodPressure and DiabetesPedigreeFunction.",10878
diabetes_correlation_heatmap.png,"Variables Pregnancies and BloodPressure are redundant, but we can’t say the same for the pair Glucose and Age.",10879
diabetes_correlation_heatmap.png,"Variables Pregnancies and Glucose are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10880
diabetes_correlation_heatmap.png,"Variables Glucose and SkinThickness are redundant, but we can’t say the same for the pair BloodPressure and Insulin.",10881
diabetes_correlation_heatmap.png,"Variables BloodPressure and Insulin are redundant, but we can’t say the same for the pair BMI and DiabetesPedigreeFunction.",10882
diabetes_correlation_heatmap.png,"Variables Glucose and BloodPressure are redundant, but we can’t say the same for the pair Pregnancies and Insulin.",10883
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair SkinThickness and Age.",10884
diabetes_correlation_heatmap.png,"Variables Insulin and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and BMI.",10885
diabetes_correlation_heatmap.png,"Variables Insulin and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and Age.",10886
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and Insulin.",10887
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair SkinThickness and Insulin.",10888
diabetes_correlation_heatmap.png,"Variables BloodPressure and Insulin are redundant, but we can’t say the same for the pair Glucose and DiabetesPedigreeFunction.",10889
diabetes_correlation_heatmap.png,"Variables Insulin and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair SkinThickness and Age.",10890
diabetes_correlation_heatmap.png,"Variables Insulin and BMI are redundant, but we can’t say the same for the pair SkinThickness and DiabetesPedigreeFunction.",10891
diabetes_correlation_heatmap.png,"Variables BloodPressure and SkinThickness are redundant, but we can’t say the same for the pair Glucose and Insulin.",10892
diabetes_correlation_heatmap.png,"Variables Insulin and BMI are redundant, but we can’t say the same for the pair Pregnancies and DiabetesPedigreeFunction.",10893
diabetes_correlation_heatmap.png,"Variables Pregnancies and Glucose are redundant, but we can’t say the same for the pair BloodPressure and Insulin.",10894
diabetes_correlation_heatmap.png,"Variables Pregnancies and Age are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10895
diabetes_correlation_heatmap.png,"Variables Glucose and BMI are redundant, but we can’t say the same for the pair Pregnancies and BloodPressure.",10896
diabetes_correlation_heatmap.png,"Variables Glucose and Insulin are redundant, but we can’t say the same for the pair BloodPressure and BMI.",10897
diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10898
diabetes_correlation_heatmap.png,"Variables SkinThickness and BMI are redundant, but we can’t say the same for the pair BloodPressure and DiabetesPedigreeFunction.",10899
diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Insulin and BMI.",10900
diabetes_correlation_heatmap.png,"Variables Glucose and Insulin are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10901
diabetes_correlation_heatmap.png,"Variables BloodPressure and BMI are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10902
diabetes_correlation_heatmap.png,"Variables Glucose and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BMI and Age.",10903
diabetes_correlation_heatmap.png,"Variables Glucose and SkinThickness are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10904
diabetes_correlation_heatmap.png,The variable Pregnancies can be discarded without risking losing information.,10905
diabetes_correlation_heatmap.png,The variable Glucose can be discarded without risking losing information.,10906
diabetes_correlation_heatmap.png,The variable BloodPressure can be discarded without risking losing information.,10907
diabetes_correlation_heatmap.png,The variable SkinThickness can be discarded without risking losing information.,10908
diabetes_correlation_heatmap.png,The variable Insulin can be discarded without risking losing information.,10909
diabetes_correlation_heatmap.png,The variable BMI can be discarded without risking losing information.,10910
diabetes_correlation_heatmap.png,The variable DiabetesPedigreeFunction can be discarded without risking losing information.,10911
diabetes_correlation_heatmap.png,The variable Age can be discarded without risking losing information.,10912
diabetes_correlation_heatmap.png,One of the variables Glucose or Pregnancies can be discarded without losing information.,10913
diabetes_correlation_heatmap.png,One of the variables BloodPressure or Pregnancies can be discarded without losing information.,10914
diabetes_correlation_heatmap.png,One of the variables SkinThickness or Pregnancies can be discarded without losing information.,10915
diabetes_correlation_heatmap.png,One of the variables Insulin or Pregnancies can be discarded without losing information.,10916
diabetes_correlation_heatmap.png,One of the variables BMI or Pregnancies can be discarded without losing information.,10917
diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or Pregnancies can be discarded without losing information.,10918
diabetes_correlation_heatmap.png,One of the variables Age or Pregnancies can be discarded without losing information.,10919
diabetes_correlation_heatmap.png,One of the variables Pregnancies or Glucose can be discarded without losing information.,10920
diabetes_correlation_heatmap.png,One of the variables BloodPressure or Glucose can be discarded without losing information.,10921
diabetes_correlation_heatmap.png,One of the variables SkinThickness or Glucose can be discarded without losing information.,10922
diabetes_correlation_heatmap.png,One of the variables Insulin or Glucose can be discarded without losing information.,10923
diabetes_correlation_heatmap.png,One of the variables BMI or Glucose can be discarded without losing information.,10924
diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or Glucose can be discarded without losing information.,10925
diabetes_correlation_heatmap.png,One of the variables Age or Glucose can be discarded without losing information.,10926
diabetes_correlation_heatmap.png,One of the variables Pregnancies or BloodPressure can be discarded without losing information.,10927
diabetes_correlation_heatmap.png,One of the variables Glucose or BloodPressure can be discarded without losing information.,10928
diabetes_correlation_heatmap.png,One of the variables SkinThickness or BloodPressure can be discarded without losing information.,10929
diabetes_correlation_heatmap.png,One of the variables Insulin or BloodPressure can be discarded without losing information.,10930
diabetes_correlation_heatmap.png,One of the variables BMI or BloodPressure can be discarded without losing information.,10931
diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or BloodPressure can be discarded without losing information.,10932
diabetes_correlation_heatmap.png,One of the variables Age or BloodPressure can be discarded without losing information.,10933
diabetes_correlation_heatmap.png,One of the variables Pregnancies or SkinThickness can be discarded without losing information.,10934
diabetes_correlation_heatmap.png,One of the variables Glucose or SkinThickness can be discarded without losing information.,10935
diabetes_correlation_heatmap.png,One of the variables BloodPressure or SkinThickness can be discarded without losing information.,10936
diabetes_correlation_heatmap.png,One of the variables Insulin or SkinThickness can be discarded without losing information.,10937
diabetes_correlation_heatmap.png,One of the variables BMI or SkinThickness can be discarded without losing information.,10938
diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or SkinThickness can be discarded without losing information.,10939
diabetes_correlation_heatmap.png,One of the variables Age or SkinThickness can be discarded without losing information.,10940
diabetes_correlation_heatmap.png,One of the variables Pregnancies or Insulin can be discarded without losing information.,10941
diabetes_correlation_heatmap.png,One of the variables Glucose or Insulin can be discarded without losing information.,10942
diabetes_correlation_heatmap.png,One of the variables BloodPressure or Insulin can be discarded without losing information.,10943
diabetes_correlation_heatmap.png,One of the variables SkinThickness or Insulin can be discarded without losing information.,10944
diabetes_correlation_heatmap.png,One of the variables BMI or Insulin can be discarded without losing information.,10945
diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or Insulin can be discarded without losing information.,10946
diabetes_correlation_heatmap.png,One of the variables Age or Insulin can be discarded without losing information.,10947
diabetes_correlation_heatmap.png,One of the variables Pregnancies or BMI can be discarded without losing information.,10948
diabetes_correlation_heatmap.png,One of the variables Glucose or BMI can be discarded without losing information.,10949
diabetes_correlation_heatmap.png,One of the variables BloodPressure or BMI can be discarded without losing information.,10950
diabetes_correlation_heatmap.png,One of the variables SkinThickness or BMI can be discarded without losing information.,10951
diabetes_correlation_heatmap.png,One of the variables Insulin or BMI can be discarded without losing information.,10952
diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or BMI can be discarded without losing information.,10953
diabetes_correlation_heatmap.png,One of the variables Age or BMI can be discarded without losing information.,10954
diabetes_correlation_heatmap.png,One of the variables Pregnancies or DiabetesPedigreeFunction can be discarded without losing information.,10955
diabetes_correlation_heatmap.png,One of the variables Glucose or DiabetesPedigreeFunction can be discarded without losing information.,10956
diabetes_correlation_heatmap.png,One of the variables BloodPressure or DiabetesPedigreeFunction can be discarded without losing information.,10957
diabetes_correlation_heatmap.png,One of the variables SkinThickness or DiabetesPedigreeFunction can be discarded without losing information.,10958
diabetes_correlation_heatmap.png,One of the variables Insulin or DiabetesPedigreeFunction can be discarded without losing information.,10959
diabetes_correlation_heatmap.png,One of the variables BMI or DiabetesPedigreeFunction can be discarded without losing information.,10960
diabetes_correlation_heatmap.png,One of the variables Age or DiabetesPedigreeFunction can be discarded without losing information.,10961
diabetes_correlation_heatmap.png,One of the variables Pregnancies or Age can be discarded without losing information.,10962
diabetes_correlation_heatmap.png,One of the variables Glucose or Age can be discarded without losing information.,10963
diabetes_correlation_heatmap.png,One of the variables BloodPressure or Age can be discarded without losing information.,10964
diabetes_correlation_heatmap.png,One of the variables SkinThickness or Age can be discarded without losing information.,10965
diabetes_correlation_heatmap.png,One of the variables Insulin or Age can be discarded without losing information.,10966
diabetes_correlation_heatmap.png,One of the variables BMI or Age can be discarded without losing information.,10967
diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or Age can be discarded without losing information.,10968
diabetes_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,10969
diabetes_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,10970
diabetes_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,10971
diabetes_histograms_numeric.png,At least 50 of the variables present outliers.,10972
diabetes_boxplots.png,At least 60 of the variables present outliers.,10973
diabetes_boxplots.png,At least 75 of the variables present outliers.,10974
diabetes_histograms_numeric.png,At least 85 of the variables present outliers.,10975
diabetes_boxplots.png,Variable Pregnancies presents some outliers.,10976
diabetes_boxplots.png,Variable Glucose presents some outliers.,10977
diabetes_histograms_numeric.png,Variable BloodPressure presents some outliers.,10978
diabetes_boxplots.png,Variable SkinThickness presents some outliers.,10979
diabetes_boxplots.png,Variable Insulin presents some outliers.,10980
diabetes_boxplots.png,Variable BMI presents some outliers.,10981
diabetes_histograms_numeric.png,Variable DiabetesPedigreeFunction presents some outliers.,10982
diabetes_histograms_numeric.png,Variable Age presents some outliers.,10983
diabetes_histograms_numeric.png,Variable Pregnancies doesn’t have any outliers.,10984
diabetes_histograms_numeric.png,Variable Glucose doesn’t have any outliers.,10985
diabetes_boxplots.png,Variable BloodPressure doesn’t have any outliers.,10986
diabetes_boxplots.png,Variable SkinThickness doesn’t have any outliers.,10987
diabetes_boxplots.png,Variable Insulin doesn’t have any outliers.,10988
diabetes_histograms_numeric.png,Variable BMI doesn’t have any outliers.,10989
diabetes_boxplots.png,Variable DiabetesPedigreeFunction doesn’t have any outliers.,10990
diabetes_histograms_numeric.png,Variable Age doesn’t have any outliers.,10991
diabetes_boxplots.png,Variable Pregnancies shows some outlier values.,10992
diabetes_boxplots.png,Variable Glucose shows some outlier values.,10993
diabetes_histograms_numeric.png,Variable BloodPressure shows some outlier values.,10994
diabetes_histograms_numeric.png,Variable SkinThickness shows some outlier values.,10995
diabetes_boxplots.png,Variable Insulin shows some outlier values.,10996
diabetes_histograms_numeric.png,Variable BMI shows some outlier values.,10997
diabetes_boxplots.png,Variable DiabetesPedigreeFunction shows some outlier values.,10998
diabetes_boxplots.png,Variable Age shows some outlier values.,10999
diabetes_histograms_numeric.png,Variable Pregnancies shows a high number of outlier values.,11000
diabetes_histograms_numeric.png,Variable Glucose shows a high number of outlier values.,11001
diabetes_boxplots.png,Variable BloodPressure shows a high number of outlier values.,11002
diabetes_histograms_numeric.png,Variable SkinThickness shows a high number of outlier values.,11003
diabetes_histograms_numeric.png,Variable Insulin shows a high number of outlier values.,11004
diabetes_histograms_numeric.png,Variable BMI shows a high number of outlier values.,11005
diabetes_histograms_numeric.png,Variable DiabetesPedigreeFunction shows a high number of outlier values.,11006
diabetes_boxplots.png,Variable Age shows a high number of outlier values.,11007
diabetes_boxplots.png,Outliers seem to be a problem in the dataset.,11008
diabetes_histograms_numeric.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11009
diabetes_histograms_numeric.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11010
diabetes_boxplots.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11011
diabetes_histograms_numeric.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11012
diabetes_histograms_numeric.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11013
diabetes_histograms_numeric.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11014
diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11015
diabetes_boxplots.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable Glucose.",11016
diabetes_histograms_numeric.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable Glucose.",11017
diabetes_boxplots.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable Glucose.",11018
diabetes_boxplots.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable Glucose.",11019
diabetes_histograms_numeric.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable Glucose.",11020
diabetes_histograms_numeric.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable Glucose.",11021
diabetes_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Glucose.",11022
diabetes_boxplots.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11023
diabetes_boxplots.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11024
diabetes_histograms_numeric.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11025
diabetes_boxplots.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11026
diabetes_histograms_numeric.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11027
diabetes_boxplots.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11028
diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11029
diabetes_boxplots.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11030
diabetes_histograms_numeric.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11031
diabetes_boxplots.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11032
diabetes_boxplots.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11033
diabetes_histograms_numeric.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11034
diabetes_boxplots.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11035
diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11036
diabetes_boxplots.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable Insulin.",11037
diabetes_histograms_numeric.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable Insulin.",11038
diabetes_histograms_numeric.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable Insulin.",11039
diabetes_boxplots.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable Insulin.",11040
diabetes_histograms_numeric.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable Insulin.",11041
diabetes_boxplots.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable Insulin.",11042
diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Insulin.",11043
diabetes_histograms_numeric.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable BMI.",11044
diabetes_histograms_numeric.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable BMI.",11045
diabetes_boxplots.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable BMI.",11046
diabetes_boxplots.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable BMI.",11047
diabetes_histograms_numeric.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable BMI.",11048
diabetes_histograms_numeric.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable BMI.",11049
diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable BMI.",11050
diabetes_boxplots.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable DiabetesPedigreeFunction.",11051
diabetes_boxplots.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable DiabetesPedigreeFunction.",11052
diabetes_boxplots.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable DiabetesPedigreeFunction.",11053
diabetes_histograms_numeric.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable DiabetesPedigreeFunction.",11054
diabetes_boxplots.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable DiabetesPedigreeFunction.",11055
diabetes_boxplots.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable DiabetesPedigreeFunction.",11056
diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable DiabetesPedigreeFunction.",11057
diabetes_histograms_numeric.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable Age.",11058
diabetes_boxplots.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable Age.",11059
diabetes_histograms_numeric.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable Age.",11060
diabetes_boxplots.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable Age.",11061
diabetes_boxplots.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable Age.",11062
diabetes_boxplots.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable Age.",11063
diabetes_histograms_numeric.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable Age.",11064
diabetes_boxplots.png,Those boxplots show that the data is not normalized.,11065
diabetes_boxplots.png,Variable Pregnancies is balanced.,11066
diabetes_boxplots.png,Variable Glucose is balanced.,11067
diabetes_histograms_numeric.png,Variable BloodPressure is balanced.,11068
diabetes_boxplots.png,Variable SkinThickness is balanced.,11069
diabetes_histograms_numeric.png,Variable Insulin is balanced.,11070
diabetes_histograms_numeric.png,Variable BMI is balanced.,11071
diabetes_boxplots.png,Variable DiabetesPedigreeFunction is balanced.,11072
diabetes_histograms_numeric.png,Variable Age is balanced.,11073
diabetes_histograms.png,The variable Pregnancies can be seen as ordinal without losing information.,11074
diabetes_histograms.png,The variable Glucose can be seen as ordinal without losing information.,11075
diabetes_histograms.png,The variable BloodPressure can be seen as ordinal without losing information.,11076
diabetes_histograms.png,The variable SkinThickness can be seen as ordinal without losing information.,11077
diabetes_histograms.png,The variable Insulin can be seen as ordinal without losing information.,11078
diabetes_histograms.png,The variable BMI can be seen as ordinal without losing information.,11079
diabetes_histograms.png,The variable DiabetesPedigreeFunction can be seen as ordinal without losing information.,11080
diabetes_histograms.png,The variable Age can be seen as ordinal without losing information.,11081
diabetes_histograms.png,The variable Pregnancies can be seen as ordinal.,11082
diabetes_histograms.png,The variable Glucose can be seen as ordinal.,11083
diabetes_histograms.png,The variable BloodPressure can be seen as ordinal.,11084
diabetes_histograms.png,The variable SkinThickness can be seen as ordinal.,11085
diabetes_histograms.png,The variable Insulin can be seen as ordinal.,11086
diabetes_histograms.png,The variable BMI can be seen as ordinal.,11087
diabetes_histograms.png,The variable DiabetesPedigreeFunction can be seen as ordinal.,11088
diabetes_histograms.png,The variable Age can be seen as ordinal.,11089
diabetes_histograms.png,"All variables, but the class, should be dealt with as numeric.",11090
diabetes_histograms.png,"All variables, but the class, should be dealt with as binary.",11091
diabetes_histograms.png,"All variables, but the class, should be dealt with as date.",11092
diabetes_histograms.png,"All variables, but the class, should be dealt with as symbolic.",11093
diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 72.,11094
diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 26.,11095
diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 38.,11096
diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 84.,11097
diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 75.,11098
diabetes_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,11099
diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",11100
diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",11101
diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",11102
diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",11103
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (A,B), as M.",11104
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (not A, B), as M.",11105
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (A, not B), as M.",11106
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as M.",11107
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (A,B), as B.",11108
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (not A, B), as B.",11109
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (A, not B), as B.",11110
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as B.",11111
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as M for any k ≤ 184.",11112
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as M for any k ≤ 184.",11113
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as M for any k ≤ 184.",11114
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as M for any k ≤ 184.",11115
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as B for any k ≤ 184.",11116
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as B for any k ≤ 184.",11117
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as B for any k ≤ 184.",11118
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as B for any k ≤ 184.",11119
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as M for any k ≤ 50.",11120
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as M for any k ≤ 50.",11121
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as M for any k ≤ 50.",11122
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as M for any k ≤ 50.",11123
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as B for any k ≤ 50.",11124
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as B for any k ≤ 50.",11125
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as B for any k ≤ 50.",11126
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as B for any k ≤ 50.",11127
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as M for any k ≤ 20.",11128
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as M for any k ≤ 20.",11129
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as M for any k ≤ 20.",11130
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as M for any k ≤ 20.",11131
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as B for any k ≤ 20.",11132
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as B for any k ≤ 20.",11133
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as B for any k ≤ 20.",11134
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as B for any k ≤ 20.",11135
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as M for any k ≤ 144.",11136
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as M for any k ≤ 144.",11137
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as M for any k ≤ 144.",11138
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as M for any k ≤ 144.",11139
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as B for any k ≤ 144.",11140
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as B for any k ≤ 144.",11141
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as B for any k ≤ 144.",11142
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as B for any k ≤ 144.",11143
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (A,B) as M.",11144
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (not A, B) as M.",11145
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (A, not B) as M.",11146
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (not A, not B) as M.",11147
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (A,B) as B.",11148
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (not A, B) as B.",11149
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (A, not B) as B.",11150
Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (not A, not B) as B.",11151
Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 844 episodes.,11152
Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 777 episodes.,11153
Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1306 episodes.,11154
Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1063 episodes.,11155
Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1206 episodes.,11156
Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 63 estimators.,11157
Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 57 estimators.,11158
Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 52 estimators.,11159
Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 65 estimators.,11160
Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 148 estimators.,11161
Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 53 estimators.,11162
Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 54 estimators.,11163
Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 116 estimators.,11164
Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 142 estimators.,11165
Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 89 estimators.,11166
Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,11167
Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,11168
Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,11169
Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,11170
Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,11171
Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,11172
Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,11173
Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,11174
Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,11175
Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,11176
Breast_Cancer_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,11177
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than its accuracy.,11178
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than its accuracy.,11179
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,11180
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than its accuracy.,11181
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than its accuracy.,11182
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,11183
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than its recall.,11184
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than its recall.,11185
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than its recall.,11186
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than its recall.,11187
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than its recall.,11188
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than its recall.,11189
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than its precision.,11190
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than its precision.,11191
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than its precision.,11192
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than its precision.,11193
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than its precision.,11194
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than its precision.,11195
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,11196
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than its specificity.,11197
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than its specificity.,11198
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,11199
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than its specificity.,11200
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than its specificity.,11201
Breast_Cancer_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,11202
Breast_Cancer_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,11203
Breast_Cancer_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,11204
Breast_Cancer_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,11205
Breast_Cancer_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,11206
Breast_Cancer_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,11207
Breast_Cancer_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,11208
Breast_Cancer_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,11209
Breast_Cancer_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,11210
Breast_Cancer_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,11211
Breast_Cancer_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,11212
Breast_Cancer_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,11213
Breast_Cancer_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,11214
Breast_Cancer_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,11215
Breast_Cancer_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,11216
Breast_Cancer_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,11217
Breast_Cancer_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,11218
Breast_Cancer_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,11219
Breast_Cancer_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,11220
Breast_Cancer_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,11221
Breast_Cancer_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,11222
Breast_Cancer_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,11223
Breast_Cancer_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,11224
Breast_Cancer_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,11225
Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 38.,11226
Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 19.,11227
Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 24.,11228
Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 46.,11229
Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 20.,11230
Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 17.,11231
Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 23.,11232
Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 14.,11233
Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 16.,11234
Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 33.,11235
Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 32.,11236
Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 29.,11237
Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 39.,11238
Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 15.,11239
Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 34.,11240
Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 42.,11241
Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 10.,11242
Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 13.,11243
Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 37.,11244
Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 31.,11245
Breast_Cancer_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,11246
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,11247
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,11248
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,11249
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,11250
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,11251
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,11252
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,11253
Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,11254
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,11255
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,11256
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,11257
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,11258
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,11259
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,11260
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,11261
Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,11262
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 73%.,11263
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 69%.,11264
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 72%.,11265
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 80%.,11266
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 75%.,11267
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 76%.,11268
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 63%.,11269
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 86%.,11270
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 90%.,11271
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 61%.,11272
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 89%.,11273
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 65%.,11274
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 84%.,11275
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 78%.,11276
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 83%.,11277
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 88%.,11278
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 85%.,11279
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 82%.,11280
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 79%.,11281
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 77%.,11282
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 68%.,11283
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 70%.,11284
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 74%.,11285
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 64%.,11286
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 87%.,11287
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 71%.,11288
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 60%.,11289
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 66%.,11290
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 67%.,11291
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 81%.,11292
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 62%.,11293
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 88%.,11294
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 89%.,11295
Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 77%.,11296
Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 71%.,11297
Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 65%.,11298
Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 77%.,11299
Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 82%.,11300
Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 80%.,11301
Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 82%.,11302
Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",11303
Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",11304
Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",11305
Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",11306
Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",11307
Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",11308
Breast_Cancer_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,11309
Breast_Cancer_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,11310
Breast_Cancer_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,11311
Breast_Cancer_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,11312
Breast_Cancer_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,11313
Breast_Cancer_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,11314
Breast_Cancer_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,11315
Breast_Cancer_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,11316
Breast_Cancer_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,11317
Breast_Cancer_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,11318
Breast_Cancer_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,11319
Breast_Cancer_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,11320
Breast_Cancer_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,11321
Breast_Cancer_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,11322
Breast_Cancer_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,11323
Breast_Cancer_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,11324
Breast_Cancer_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,11325
Breast_Cancer_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,11326
Breast_Cancer_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,11327
Breast_Cancer_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,11328
Breast_Cancer_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,11329
Breast_Cancer_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,11330
Breast_Cancer_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,11331
Breast_Cancer_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,11332
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 2.,11333
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 2.,11334
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 3.,11335
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 3.,11336
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 4.,11337
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 4.,11338
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 5.,11339
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 5.,11340
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 6.,11341
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 6.,11342
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 7.,11343
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 7.,11344
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 8.,11345
Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 8.,11346
Breast_Cancer_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",11347
Breast_Cancer_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",11348
Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",11349
Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",11350
Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",11351
Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",11352
Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",11353
Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",11354
Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",11355
Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",11356
Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",11357
Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",11358
Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",11359
Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",11360
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,11361
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,11362
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,11363
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,11364
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,11365
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,11366
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,11367
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,11368
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,11369
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,11370
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,11371
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,11372
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,11373
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,11374
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,11375
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,11376
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,11377
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,11378
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,11379
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,11380
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,11381
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,11382
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,11383
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,11384
Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,11385
Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,11386
Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,11387
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable texture_mean.,11388
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable texture_mean.,11389
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable texture_mean.,11390
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable texture_mean.,11391
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable texture_mean.,11392
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable texture_mean.,11393
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable texture_mean.,11394
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable texture_mean.,11395
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable texture_mean.,11396
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable perimeter_mean.,11397
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable perimeter_mean.,11398
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable perimeter_mean.,11399
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable perimeter_mean.,11400
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable perimeter_mean.,11401
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable perimeter_mean.,11402
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable perimeter_mean.,11403
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable perimeter_mean.,11404
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable perimeter_mean.,11405
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable texture_se.,11406
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable texture_se.,11407
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable texture_se.,11408
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable texture_se.,11409
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable texture_se.,11410
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable texture_se.,11411
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable texture_se.,11412
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable texture_se.,11413
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable texture_se.,11414
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable perimeter_se.,11415
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable perimeter_se.,11416
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable perimeter_se.,11417
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable perimeter_se.,11418
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable perimeter_se.,11419
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable perimeter_se.,11420
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable perimeter_se.,11421
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable perimeter_se.,11422
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable perimeter_se.,11423
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable area_se.,11424
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable area_se.,11425
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable area_se.,11426
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable area_se.,11427
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable area_se.,11428
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable area_se.,11429
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable area_se.,11430
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable area_se.,11431
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable area_se.,11432
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable smoothness_se.,11433
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable smoothness_se.,11434
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable smoothness_se.,11435
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable smoothness_se.,11436
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable smoothness_se.,11437
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable smoothness_se.,11438
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable smoothness_se.,11439
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable smoothness_se.,11440
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable smoothness_se.,11441
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable symmetry_se.,11442
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable symmetry_se.,11443
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable symmetry_se.,11444
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable symmetry_se.,11445
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable symmetry_se.,11446
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable symmetry_se.,11447
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable symmetry_se.,11448
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable symmetry_se.,11449
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable symmetry_se.,11450
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable radius_worst.,11451
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable radius_worst.,11452
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable radius_worst.,11453
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable radius_worst.,11454
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable radius_worst.,11455
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable radius_worst.,11456
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable radius_worst.,11457
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable radius_worst.,11458
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable radius_worst.,11459
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable texture_worst.,11460
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable texture_worst.,11461
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable texture_worst.,11462
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable texture_worst.,11463
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable texture_worst.,11464
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable texture_worst.,11465
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable texture_worst.,11466
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable texture_worst.,11467
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable texture_worst.,11468
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable perimeter_worst.,11469
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable perimeter_worst.,11470
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable perimeter_worst.,11471
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable perimeter_worst.,11472
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable perimeter_worst.,11473
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable perimeter_worst.,11474
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable symmetry_se previously than variable perimeter_worst.,11475
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable perimeter_worst.,11476
Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable perimeter_worst.,11477
Breast_Cancer_pca.png,The first 2 principal components are enough for explaining half the data variance.,11478
Breast_Cancer_pca.png,The first 3 principal components are enough for explaining half the data variance.,11479
Breast_Cancer_pca.png,The first 4 principal components are enough for explaining half the data variance.,11480
Breast_Cancer_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,11481
Breast_Cancer_correlation_heatmap.png,Removing variable texture_mean might improve the training of decision trees .,11482
Breast_Cancer_correlation_heatmap.png,Removing variable perimeter_mean might improve the training of decision trees .,11483
Breast_Cancer_correlation_heatmap.png,Removing variable texture_se might improve the training of decision trees .,11484
Breast_Cancer_correlation_heatmap.png,Removing variable perimeter_se might improve the training of decision trees .,11485
Breast_Cancer_correlation_heatmap.png,Removing variable area_se might improve the training of decision trees .,11486
Breast_Cancer_correlation_heatmap.png,Removing variable smoothness_se might improve the training of decision trees .,11487
Breast_Cancer_correlation_heatmap.png,Removing variable symmetry_se might improve the training of decision trees .,11488
Breast_Cancer_correlation_heatmap.png,Removing variable radius_worst might improve the training of decision trees .,11489
Breast_Cancer_correlation_heatmap.png,Removing variable texture_worst might improve the training of decision trees .,11490
Breast_Cancer_correlation_heatmap.png,Removing variable perimeter_worst might improve the training of decision trees .,11491
Breast_Cancer_histograms.png,"Not knowing the semantics of texture_mean variable, dummification could have been a more adequate codification.",11492
Breast_Cancer_histograms.png,"Not knowing the semantics of perimeter_mean variable, dummification could have been a more adequate codification.",11493
Breast_Cancer_histograms.png,"Not knowing the semantics of texture_se variable, dummification could have been a more adequate codification.",11494
Breast_Cancer_histograms.png,"Not knowing the semantics of perimeter_se variable, dummification could have been a more adequate codification.",11495
Breast_Cancer_histograms.png,"Not knowing the semantics of area_se variable, dummification could have been a more adequate codification.",11496
Breast_Cancer_histograms.png,"Not knowing the semantics of smoothness_se variable, dummification could have been a more adequate codification.",11497
Breast_Cancer_histograms.png,"Not knowing the semantics of symmetry_se variable, dummification could have been a more adequate codification.",11498
Breast_Cancer_histograms.png,"Not knowing the semantics of radius_worst variable, dummification could have been a more adequate codification.",11499
Breast_Cancer_histograms.png,"Not knowing the semantics of texture_worst variable, dummification could have been a more adequate codification.",11500
Breast_Cancer_histograms.png,"Not knowing the semantics of perimeter_worst variable, dummification could have been a more adequate codification.",11501
Breast_Cancer_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,11502
Breast_Cancer_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",11503
Breast_Cancer_histograms.png,It is better to drop the variable texture_mean than removing all records with missing values.,11504
Breast_Cancer_histograms.png,It is better to drop the variable perimeter_mean than removing all records with missing values.,11505
Breast_Cancer_histograms.png,It is better to drop the variable texture_se than removing all records with missing values.,11506
Breast_Cancer_histograms.png,It is better to drop the variable perimeter_se than removing all records with missing values.,11507
Breast_Cancer_histograms.png,It is better to drop the variable area_se than removing all records with missing values.,11508
Breast_Cancer_histograms.png,It is better to drop the variable smoothness_se than removing all records with missing values.,11509
Breast_Cancer_histograms.png,It is better to drop the variable symmetry_se than removing all records with missing values.,11510
Breast_Cancer_histograms.png,It is better to drop the variable radius_worst than removing all records with missing values.,11511
Breast_Cancer_histograms.png,It is better to drop the variable texture_worst than removing all records with missing values.,11512
Breast_Cancer_histograms.png,It is better to drop the variable perimeter_worst than removing all records with missing values.,11513
Breast_Cancer_histograms.png,"Given the usual semantics of texture_mean variable, dummification would have been a better codification.",11514
Breast_Cancer_histograms.png,"Given the usual semantics of perimeter_mean variable, dummification would have been a better codification.",11515
Breast_Cancer_histograms.png,"Given the usual semantics of texture_se variable, dummification would have been a better codification.",11516
Breast_Cancer_histograms.png,"Given the usual semantics of perimeter_se variable, dummification would have been a better codification.",11517
Breast_Cancer_histograms.png,"Given the usual semantics of area_se variable, dummification would have been a better codification.",11518
Breast_Cancer_histograms.png,"Given the usual semantics of smoothness_se variable, dummification would have been a better codification.",11519
Breast_Cancer_histograms.png,"Given the usual semantics of symmetry_se variable, dummification would have been a better codification.",11520
Breast_Cancer_histograms.png,"Given the usual semantics of radius_worst variable, dummification would have been a better codification.",11521
Breast_Cancer_histograms.png,"Given the usual semantics of texture_worst variable, dummification would have been a better codification.",11522
Breast_Cancer_histograms.png,"Given the usual semantics of perimeter_worst variable, dummification would have been a better codification.",11523
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of texture_mean seems to be promising.",11524
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11525
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11526
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11527
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11528
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11529
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of texture_mean seems to be promising.",11530
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of texture_mean seems to be promising.",11531
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of texture_mean seems to be promising.",11532
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11533
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11534
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11535
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11536
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11537
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11538
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11539
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11540
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11541
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of texture_se seems to be promising.",11542
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of texture_se seems to be promising.",11543
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of texture_se seems to be promising.",11544
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of texture_se seems to be promising.",11545
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of texture_se seems to be promising.",11546
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of texture_se seems to be promising.",11547
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of texture_se seems to be promising.",11548
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of texture_se seems to be promising.",11549
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of texture_se seems to be promising.",11550
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of perimeter_se seems to be promising.",11551
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of perimeter_se seems to be promising.",11552
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11553
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11554
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11555
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11556
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of perimeter_se seems to be promising.",11557
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of perimeter_se seems to be promising.",11558
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of perimeter_se seems to be promising.",11559
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of area_se seems to be promising.",11560
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of area_se seems to be promising.",11561
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of area_se seems to be promising.",11562
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of area_se seems to be promising.",11563
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of area_se seems to be promising.",11564
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of area_se seems to be promising.",11565
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of area_se seems to be promising.",11566
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of area_se seems to be promising.",11567
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of area_se seems to be promising.",11568
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of smoothness_se seems to be promising.",11569
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of smoothness_se seems to be promising.",11570
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11571
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11572
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11573
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11574
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of smoothness_se seems to be promising.",11575
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of smoothness_se seems to be promising.",11576
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of smoothness_se seems to be promising.",11577
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of symmetry_se seems to be promising.",11578
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of symmetry_se seems to be promising.",11579
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11580
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11581
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11582
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11583
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of symmetry_se seems to be promising.",11584
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of symmetry_se seems to be promising.",11585
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of symmetry_se seems to be promising.",11586
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of radius_worst seems to be promising.",11587
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of radius_worst seems to be promising.",11588
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11589
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11590
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11591
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11592
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11593
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of radius_worst seems to be promising.",11594
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of radius_worst seems to be promising.",11595
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of texture_worst seems to be promising.",11596
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of texture_worst seems to be promising.",11597
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11598
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11599
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11600
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11601
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11602
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of texture_worst seems to be promising.",11603
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of texture_worst seems to be promising.",11604
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11605
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11606
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11607
Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11608
Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11609
Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11610
Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11611
Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11612
Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11613
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and texture_mean seems to be promising.,11614
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and texture_mean seems to be promising.,11615
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and texture_mean seems to be promising.,11616
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and texture_mean seems to be promising.,11617
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and texture_mean seems to be promising.,11618
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and texture_mean seems to be promising.,11619
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and texture_mean seems to be promising.,11620
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and texture_mean seems to be promising.,11621
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and texture_mean seems to be promising.,11622
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and perimeter_mean seems to be promising.,11623
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and perimeter_mean seems to be promising.,11624
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and perimeter_mean seems to be promising.,11625
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and perimeter_mean seems to be promising.,11626
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and perimeter_mean seems to be promising.,11627
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and perimeter_mean seems to be promising.,11628
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and perimeter_mean seems to be promising.,11629
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and perimeter_mean seems to be promising.,11630
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and perimeter_mean seems to be promising.,11631
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and texture_se seems to be promising.,11632
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and texture_se seems to be promising.,11633
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and texture_se seems to be promising.,11634
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and texture_se seems to be promising.,11635
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and texture_se seems to be promising.,11636
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and texture_se seems to be promising.,11637
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and texture_se seems to be promising.,11638
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and texture_se seems to be promising.,11639
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and texture_se seems to be promising.,11640
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and perimeter_se seems to be promising.,11641
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and perimeter_se seems to be promising.,11642
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and perimeter_se seems to be promising.,11643
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and perimeter_se seems to be promising.,11644
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and perimeter_se seems to be promising.,11645
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and perimeter_se seems to be promising.,11646
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and perimeter_se seems to be promising.,11647
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and perimeter_se seems to be promising.,11648
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and perimeter_se seems to be promising.,11649
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and area_se seems to be promising.,11650
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and area_se seems to be promising.,11651
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and area_se seems to be promising.,11652
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and area_se seems to be promising.,11653
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and area_se seems to be promising.,11654
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and area_se seems to be promising.,11655
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and area_se seems to be promising.,11656
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and area_se seems to be promising.,11657
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and area_se seems to be promising.,11658
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and smoothness_se seems to be promising.,11659
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and smoothness_se seems to be promising.,11660
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and smoothness_se seems to be promising.,11661
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and smoothness_se seems to be promising.,11662
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and smoothness_se seems to be promising.,11663
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and smoothness_se seems to be promising.,11664
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and smoothness_se seems to be promising.,11665
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and smoothness_se seems to be promising.,11666
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and smoothness_se seems to be promising.,11667
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and symmetry_se seems to be promising.,11668
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and symmetry_se seems to be promising.,11669
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and symmetry_se seems to be promising.,11670
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and symmetry_se seems to be promising.,11671
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and symmetry_se seems to be promising.,11672
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and symmetry_se seems to be promising.,11673
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and symmetry_se seems to be promising.,11674
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and symmetry_se seems to be promising.,11675
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and symmetry_se seems to be promising.,11676
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and radius_worst seems to be promising.,11677
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and radius_worst seems to be promising.,11678
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and radius_worst seems to be promising.,11679
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and radius_worst seems to be promising.,11680
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and radius_worst seems to be promising.,11681
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and radius_worst seems to be promising.,11682
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and radius_worst seems to be promising.,11683
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and radius_worst seems to be promising.,11684
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and radius_worst seems to be promising.,11685
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and texture_worst seems to be promising.,11686
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and texture_worst seems to be promising.,11687
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and texture_worst seems to be promising.,11688
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and texture_worst seems to be promising.,11689
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and texture_worst seems to be promising.,11690
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and texture_worst seems to be promising.,11691
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and texture_worst seems to be promising.,11692
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and texture_worst seems to be promising.,11693
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and texture_worst seems to be promising.,11694
Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and perimeter_worst seems to be promising.,11695
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and perimeter_worst seems to be promising.,11696
Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and perimeter_worst seems to be promising.,11697
Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and perimeter_worst seems to be promising.,11698
Breast_Cancer_histograms.png,Feature generation based on both variables area_se and perimeter_worst seems to be promising.,11699
Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and perimeter_worst seems to be promising.,11700
Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and perimeter_worst seems to be promising.,11701
Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and perimeter_worst seems to be promising.,11702
Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and perimeter_worst seems to be promising.,11703
Breast_Cancer_histograms.png,The variable texture_mean can be coded as ordinal without losing information.,11799
Breast_Cancer_histograms.png,The variable perimeter_mean can be coded as ordinal without losing information.,11800
Breast_Cancer_histograms.png,The variable texture_se can be coded as ordinal without losing information.,11801
Breast_Cancer_histograms.png,The variable perimeter_se can be coded as ordinal without losing information.,11802
Breast_Cancer_histograms.png,The variable area_se can be coded as ordinal without losing information.,11803
Breast_Cancer_histograms.png,The variable smoothness_se can be coded as ordinal without losing information.,11804
Breast_Cancer_histograms.png,The variable symmetry_se can be coded as ordinal without losing information.,11805
Breast_Cancer_histograms.png,The variable radius_worst can be coded as ordinal without losing information.,11806
Breast_Cancer_histograms.png,The variable texture_worst can be coded as ordinal without losing information.,11807
Breast_Cancer_histograms.png,The variable perimeter_worst can be coded as ordinal without losing information.,11808
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean variable, dummification would be the most adequate encoding.",11809
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean variable, dummification would be the most adequate encoding.",11810
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se variable, dummification would be the most adequate encoding.",11811
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se variable, dummification would be the most adequate encoding.",11812
Breast_Cancer_histograms.png,"Considering the common semantics for area_se variable, dummification would be the most adequate encoding.",11813
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se variable, dummification would be the most adequate encoding.",11814
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se variable, dummification would be the most adequate encoding.",11815
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst variable, dummification would be the most adequate encoding.",11816
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst variable, dummification would be the most adequate encoding.",11817
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst variable, dummification would be the most adequate encoding.",11818
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11819
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11820
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11821
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11822
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11823
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11824
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11825
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11826
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11827
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11828
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11829
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11830
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11831
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11832
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11833
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11834
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11835
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and perimeter_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11836
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11837
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11838
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11839
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11840
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11841
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11842
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11843
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11844
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11845
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11846
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11847
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11848
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11849
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11850
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11851
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11852
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11853
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11854
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11855
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11856
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11857
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11858
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11859
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11860
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11861
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11862
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11863
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11864
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11865
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11866
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11867
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11868
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11869
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11870
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11871
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11872
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11873
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11874
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11875
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11876
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11877
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11878
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11879
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11880
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11881
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11882
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11883
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11884
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11885
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11886
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11887
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11888
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11889
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11890
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11891
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11892
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11893
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11894
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11895
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11896
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11897
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11898
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11899
Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11900
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11901
Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11902
Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11903
Breast_Cancer_histograms.png,"Considering the common semantics for area_se and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11904
Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11905
Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11906
Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11907
Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11908
Breast_Cancer_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,11909
Breast_Cancer_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,11910
Breast_Cancer_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,11911
Breast_Cancer_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",11912
Breast_Cancer_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",11913
Breast_Cancer_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",11914
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and texture_mean seem to be useful for classification tasks.,11915
Breast_Cancer_correlation_heatmap.png,Variables texture_se and texture_mean seem to be useful for classification tasks.,11916
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and texture_mean seem to be useful for classification tasks.,11917
Breast_Cancer_correlation_heatmap.png,Variables area_se and texture_mean seem to be useful for classification tasks.,11918
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and texture_mean seem to be useful for classification tasks.,11919
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and texture_mean seem to be useful for classification tasks.,11920
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and texture_mean seem to be useful for classification tasks.,11921
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and texture_mean seem to be useful for classification tasks.,11922
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and texture_mean seem to be useful for classification tasks.,11923
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and perimeter_mean seem to be useful for classification tasks.,11924
Breast_Cancer_correlation_heatmap.png,Variables texture_se and perimeter_mean seem to be useful for classification tasks.,11925
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and perimeter_mean seem to be useful for classification tasks.,11926
Breast_Cancer_correlation_heatmap.png,Variables area_se and perimeter_mean seem to be useful for classification tasks.,11927
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and perimeter_mean seem to be useful for classification tasks.,11928
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_mean seem to be useful for classification tasks.,11929
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and perimeter_mean seem to be useful for classification tasks.,11930
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and perimeter_mean seem to be useful for classification tasks.,11931
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and perimeter_mean seem to be useful for classification tasks.,11932
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and texture_se seem to be useful for classification tasks.,11933
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and texture_se seem to be useful for classification tasks.,11934
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and texture_se seem to be useful for classification tasks.,11935
Breast_Cancer_correlation_heatmap.png,Variables area_se and texture_se seem to be useful for classification tasks.,11936
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and texture_se seem to be useful for classification tasks.,11937
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and texture_se seem to be useful for classification tasks.,11938
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and texture_se seem to be useful for classification tasks.,11939
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and texture_se seem to be useful for classification tasks.,11940
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and texture_se seem to be useful for classification tasks.,11941
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and perimeter_se seem to be useful for classification tasks.,11942
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and perimeter_se seem to be useful for classification tasks.,11943
Breast_Cancer_correlation_heatmap.png,Variables texture_se and perimeter_se seem to be useful for classification tasks.,11944
Breast_Cancer_correlation_heatmap.png,Variables area_se and perimeter_se seem to be useful for classification tasks.,11945
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and perimeter_se seem to be useful for classification tasks.,11946
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_se seem to be useful for classification tasks.,11947
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and perimeter_se seem to be useful for classification tasks.,11948
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and perimeter_se seem to be useful for classification tasks.,11949
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and perimeter_se seem to be useful for classification tasks.,11950
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and area_se seem to be useful for classification tasks.,11951
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and area_se seem to be useful for classification tasks.,11952
Breast_Cancer_correlation_heatmap.png,Variables texture_se and area_se seem to be useful for classification tasks.,11953
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and area_se seem to be useful for classification tasks.,11954
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and area_se seem to be useful for classification tasks.,11955
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and area_se seem to be useful for classification tasks.,11956
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and area_se seem to be useful for classification tasks.,11957
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and area_se seem to be useful for classification tasks.,11958
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and area_se seem to be useful for classification tasks.,11959
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and smoothness_se seem to be useful for classification tasks.,11960
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and smoothness_se seem to be useful for classification tasks.,11961
Breast_Cancer_correlation_heatmap.png,Variables texture_se and smoothness_se seem to be useful for classification tasks.,11962
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and smoothness_se seem to be useful for classification tasks.,11963
Breast_Cancer_correlation_heatmap.png,Variables area_se and smoothness_se seem to be useful for classification tasks.,11964
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and smoothness_se seem to be useful for classification tasks.,11965
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and smoothness_se seem to be useful for classification tasks.,11966
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and smoothness_se seem to be useful for classification tasks.,11967
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and smoothness_se seem to be useful for classification tasks.,11968
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and symmetry_se seem to be useful for classification tasks.,11969
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and symmetry_se seem to be useful for classification tasks.,11970
Breast_Cancer_correlation_heatmap.png,Variables texture_se and symmetry_se seem to be useful for classification tasks.,11971
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and symmetry_se seem to be useful for classification tasks.,11972
Breast_Cancer_correlation_heatmap.png,Variables area_se and symmetry_se seem to be useful for classification tasks.,11973
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and symmetry_se seem to be useful for classification tasks.,11974
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and symmetry_se seem to be useful for classification tasks.,11975
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and symmetry_se seem to be useful for classification tasks.,11976
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and symmetry_se seem to be useful for classification tasks.,11977
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and radius_worst seem to be useful for classification tasks.,11978
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and radius_worst seem to be useful for classification tasks.,11979
Breast_Cancer_correlation_heatmap.png,Variables texture_se and radius_worst seem to be useful for classification tasks.,11980
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and radius_worst seem to be useful for classification tasks.,11981
Breast_Cancer_correlation_heatmap.png,Variables area_se and radius_worst seem to be useful for classification tasks.,11982
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and radius_worst seem to be useful for classification tasks.,11983
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and radius_worst seem to be useful for classification tasks.,11984
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and radius_worst seem to be useful for classification tasks.,11985
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and radius_worst seem to be useful for classification tasks.,11986
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and texture_worst seem to be useful for classification tasks.,11987
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and texture_worst seem to be useful for classification tasks.,11988
Breast_Cancer_correlation_heatmap.png,Variables texture_se and texture_worst seem to be useful for classification tasks.,11989
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and texture_worst seem to be useful for classification tasks.,11990
Breast_Cancer_correlation_heatmap.png,Variables area_se and texture_worst seem to be useful for classification tasks.,11991
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and texture_worst seem to be useful for classification tasks.,11992
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and texture_worst seem to be useful for classification tasks.,11993
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and texture_worst seem to be useful for classification tasks.,11994
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and texture_worst seem to be useful for classification tasks.,11995
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and perimeter_worst seem to be useful for classification tasks.,11996
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and perimeter_worst seem to be useful for classification tasks.,11997
Breast_Cancer_correlation_heatmap.png,Variables texture_se and perimeter_worst seem to be useful for classification tasks.,11998
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and perimeter_worst seem to be useful for classification tasks.,11999
Breast_Cancer_correlation_heatmap.png,Variables area_se and perimeter_worst seem to be useful for classification tasks.,12000
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and perimeter_worst seem to be useful for classification tasks.,12001
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_worst seem to be useful for classification tasks.,12002
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and perimeter_worst seem to be useful for classification tasks.,12003
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and perimeter_worst seem to be useful for classification tasks.,12004
Breast_Cancer_correlation_heatmap.png,Variable texture_mean seems to be relevant for the majority of mining tasks.,12005
Breast_Cancer_correlation_heatmap.png,Variable perimeter_mean seems to be relevant for the majority of mining tasks.,12006
Breast_Cancer_correlation_heatmap.png,Variable texture_se seems to be relevant for the majority of mining tasks.,12007
Breast_Cancer_correlation_heatmap.png,Variable perimeter_se seems to be relevant for the majority of mining tasks.,12008
Breast_Cancer_correlation_heatmap.png,Variable area_se seems to be relevant for the majority of mining tasks.,12009
Breast_Cancer_correlation_heatmap.png,Variable smoothness_se seems to be relevant for the majority of mining tasks.,12010
Breast_Cancer_correlation_heatmap.png,Variable symmetry_se seems to be relevant for the majority of mining tasks.,12011
Breast_Cancer_correlation_heatmap.png,Variable radius_worst seems to be relevant for the majority of mining tasks.,12012
Breast_Cancer_correlation_heatmap.png,Variable texture_worst seems to be relevant for the majority of mining tasks.,12013
Breast_Cancer_correlation_heatmap.png,Variable perimeter_worst seems to be relevant for the majority of mining tasks.,12014
Breast_Cancer_decision_tree.png,Variable texture_mean is one of the most relevant variables.,12015
Breast_Cancer_decision_tree.png,Variable perimeter_mean is one of the most relevant variables.,12016
Breast_Cancer_decision_tree.png,Variable texture_se is one of the most relevant variables.,12017
Breast_Cancer_decision_tree.png,Variable perimeter_se is one of the most relevant variables.,12018
Breast_Cancer_decision_tree.png,Variable area_se is one of the most relevant variables.,12019
Breast_Cancer_decision_tree.png,Variable smoothness_se is one of the most relevant variables.,12020
Breast_Cancer_decision_tree.png,Variable symmetry_se is one of the most relevant variables.,12021
Breast_Cancer_decision_tree.png,Variable radius_worst is one of the most relevant variables.,12022
Breast_Cancer_decision_tree.png,Variable texture_worst is one of the most relevant variables.,12023
Breast_Cancer_decision_tree.png,Variable perimeter_worst is one of the most relevant variables.,12024
Breast_Cancer_decision_tree.png,It is possible to state that texture_mean is the first most discriminative variable regarding the class.,12025
Breast_Cancer_decision_tree.png,It is possible to state that perimeter_mean is the first most discriminative variable regarding the class.,12026
Breast_Cancer_decision_tree.png,It is possible to state that texture_se is the first most discriminative variable regarding the class.,12027
Breast_Cancer_decision_tree.png,It is possible to state that perimeter_se is the first most discriminative variable regarding the class.,12028
Breast_Cancer_decision_tree.png,It is possible to state that area_se is the first most discriminative variable regarding the class.,12029
Breast_Cancer_decision_tree.png,It is possible to state that smoothness_se is the first most discriminative variable regarding the class.,12030
Breast_Cancer_decision_tree.png,It is possible to state that symmetry_se is the first most discriminative variable regarding the class.,12031
Breast_Cancer_decision_tree.png,It is possible to state that radius_worst is the first most discriminative variable regarding the class.,12032
Breast_Cancer_decision_tree.png,It is possible to state that texture_worst is the first most discriminative variable regarding the class.,12033
Breast_Cancer_decision_tree.png,It is possible to state that perimeter_worst is the first most discriminative variable regarding the class.,12034
Breast_Cancer_decision_tree.png,It is possible to state that texture_mean is the second most discriminative variable regarding the class.,12035
Breast_Cancer_decision_tree.png,It is possible to state that perimeter_mean is the second most discriminative variable regarding the class.,12036
Breast_Cancer_decision_tree.png,It is possible to state that texture_se is the second most discriminative variable regarding the class.,12037
Breast_Cancer_decision_tree.png,It is possible to state that perimeter_se is the second most discriminative variable regarding the class.,12038
Breast_Cancer_decision_tree.png,It is possible to state that area_se is the second most discriminative variable regarding the class.,12039
Breast_Cancer_decision_tree.png,It is possible to state that smoothness_se is the second most discriminative variable regarding the class.,12040
Breast_Cancer_decision_tree.png,It is possible to state that symmetry_se is the second most discriminative variable regarding the class.,12041
Breast_Cancer_decision_tree.png,It is possible to state that radius_worst is the second most discriminative variable regarding the class.,12042
Breast_Cancer_decision_tree.png,It is possible to state that texture_worst is the second most discriminative variable regarding the class.,12043
Breast_Cancer_decision_tree.png,It is possible to state that perimeter_worst is the second most discriminative variable regarding the class.,12044
Breast_Cancer_decision_tree.png,"The variable texture_mean discriminates between the target values, as shown in the decision tree.",12045
Breast_Cancer_decision_tree.png,"The variable perimeter_mean discriminates between the target values, as shown in the decision tree.",12046
Breast_Cancer_decision_tree.png,"The variable texture_se discriminates between the target values, as shown in the decision tree.",12047
Breast_Cancer_decision_tree.png,"The variable perimeter_se discriminates between the target values, as shown in the decision tree.",12048
Breast_Cancer_decision_tree.png,"The variable area_se discriminates between the target values, as shown in the decision tree.",12049
Breast_Cancer_decision_tree.png,"The variable smoothness_se discriminates between the target values, as shown in the decision tree.",12050
Breast_Cancer_decision_tree.png,"The variable symmetry_se discriminates between the target values, as shown in the decision tree.",12051
Breast_Cancer_decision_tree.png,"The variable radius_worst discriminates between the target values, as shown in the decision tree.",12052
Breast_Cancer_decision_tree.png,"The variable texture_worst discriminates between the target values, as shown in the decision tree.",12053
Breast_Cancer_decision_tree.png,"The variable perimeter_worst discriminates between the target values, as shown in the decision tree.",12054
Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the two most relevant features.,12055
Breast_Cancer_decision_tree.png,The variable perimeter_mean seems to be one of the two most relevant features.,12056
Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the two most relevant features.,12057
Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the two most relevant features.,12058
Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the two most relevant features.,12059
Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the two most relevant features.,12060
Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the two most relevant features.,12061
Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the two most relevant features.,12062
Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the two most relevant features.,12063
Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the two most relevant features.,12064
Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the three most relevant features.,12065
Breast_Cancer_decision_tree.png,The variable perimeter_mean seems to be one of the three most relevant features.,12066
Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the three most relevant features.,12067
Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the three most relevant features.,12068
Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the three most relevant features.,12069
Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the three most relevant features.,12070
Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the three most relevant features.,12071
Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the three most relevant features.,12072
Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the three most relevant features.,12073
Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the three most relevant features.,12074
Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the four most relevant features.,12075
Breast_Cancer_decision_tree.png,The variable perimeter_mean seems to be one of the four most relevant features.,12076
Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the four most relevant features.,12077
Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the four most relevant features.,12078
Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the four most relevant features.,12079
Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the four most relevant features.,12080
Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the four most relevant features.,12081
Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the four most relevant features.,12082
Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the four most relevant features.,12083
Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the four most relevant features.,12084
Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the five most relevant features.,12085
Breast_Cancer_decision_tree.png,The variable perimeter_mean seems to be one of the five most relevant features.,12086
Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the five most relevant features.,12087
Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the five most relevant features.,12088
Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the five most relevant features.,12089
Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the five most relevant features.,12090
Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the five most relevant features.,12091
Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the five most relevant features.,12092
Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the five most relevant features.,12093
Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the five most relevant features.,12094
Breast_Cancer_decision_tree.png,It is clear that variable texture_mean is one of the two most relevant features.,12095
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_mean is one of the two most relevant features.,12096
Breast_Cancer_decision_tree.png,It is clear that variable texture_se is one of the two most relevant features.,12097
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_se is one of the two most relevant features.,12098
Breast_Cancer_decision_tree.png,It is clear that variable area_se is one of the two most relevant features.,12099
Breast_Cancer_decision_tree.png,It is clear that variable smoothness_se is one of the two most relevant features.,12100
Breast_Cancer_decision_tree.png,It is clear that variable symmetry_se is one of the two most relevant features.,12101
Breast_Cancer_decision_tree.png,It is clear that variable radius_worst is one of the two most relevant features.,12102
Breast_Cancer_decision_tree.png,It is clear that variable texture_worst is one of the two most relevant features.,12103
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_worst is one of the two most relevant features.,12104
Breast_Cancer_decision_tree.png,It is clear that variable texture_mean is one of the three most relevant features.,12105
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_mean is one of the three most relevant features.,12106
Breast_Cancer_decision_tree.png,It is clear that variable texture_se is one of the three most relevant features.,12107
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_se is one of the three most relevant features.,12108
Breast_Cancer_decision_tree.png,It is clear that variable area_se is one of the three most relevant features.,12109
Breast_Cancer_decision_tree.png,It is clear that variable smoothness_se is one of the three most relevant features.,12110
Breast_Cancer_decision_tree.png,It is clear that variable symmetry_se is one of the three most relevant features.,12111
Breast_Cancer_decision_tree.png,It is clear that variable radius_worst is one of the three most relevant features.,12112
Breast_Cancer_decision_tree.png,It is clear that variable texture_worst is one of the three most relevant features.,12113
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_worst is one of the three most relevant features.,12114
Breast_Cancer_decision_tree.png,It is clear that variable texture_mean is one of the four most relevant features.,12115
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_mean is one of the four most relevant features.,12116
Breast_Cancer_decision_tree.png,It is clear that variable texture_se is one of the four most relevant features.,12117
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_se is one of the four most relevant features.,12118
Breast_Cancer_decision_tree.png,It is clear that variable area_se is one of the four most relevant features.,12119
Breast_Cancer_decision_tree.png,It is clear that variable smoothness_se is one of the four most relevant features.,12120
Breast_Cancer_decision_tree.png,It is clear that variable symmetry_se is one of the four most relevant features.,12121
Breast_Cancer_decision_tree.png,It is clear that variable radius_worst is one of the four most relevant features.,12122
Breast_Cancer_decision_tree.png,It is clear that variable texture_worst is one of the four most relevant features.,12123
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_worst is one of the four most relevant features.,12124
Breast_Cancer_decision_tree.png,It is clear that variable texture_mean is one of the five most relevant features.,12125
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_mean is one of the five most relevant features.,12126
Breast_Cancer_decision_tree.png,It is clear that variable texture_se is one of the five most relevant features.,12127
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_se is one of the five most relevant features.,12128
Breast_Cancer_decision_tree.png,It is clear that variable area_se is one of the five most relevant features.,12129
Breast_Cancer_decision_tree.png,It is clear that variable smoothness_se is one of the five most relevant features.,12130
Breast_Cancer_decision_tree.png,It is clear that variable symmetry_se is one of the five most relevant features.,12131
Breast_Cancer_decision_tree.png,It is clear that variable radius_worst is one of the five most relevant features.,12132
Breast_Cancer_decision_tree.png,It is clear that variable texture_worst is one of the five most relevant features.,12133
Breast_Cancer_decision_tree.png,It is clear that variable perimeter_worst is one of the five most relevant features.,12134
Breast_Cancer_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",12135
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and texture_mean are redundant.,12136
Breast_Cancer_correlation_heatmap.png,Variables texture_se and texture_mean are redundant.,12137
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and texture_mean are redundant.,12138
Breast_Cancer_correlation_heatmap.png,Variables area_se and texture_mean are redundant.,12139
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and texture_mean are redundant.,12140
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and texture_mean are redundant.,12141
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and texture_mean are redundant.,12142
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and texture_mean are redundant.,12143
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and texture_mean are redundant.,12144
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and perimeter_mean are redundant.,12145
Breast_Cancer_correlation_heatmap.png,Variables texture_se and perimeter_mean are redundant.,12146
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and perimeter_mean are redundant.,12147
Breast_Cancer_correlation_heatmap.png,Variables area_se and perimeter_mean are redundant.,12148
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and perimeter_mean are redundant.,12149
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_mean are redundant.,12150
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and perimeter_mean are redundant.,12151
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and perimeter_mean are redundant.,12152
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and perimeter_mean are redundant.,12153
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and texture_se are redundant.,12154
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and texture_se are redundant.,12155
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and texture_se are redundant.,12156
Breast_Cancer_correlation_heatmap.png,Variables area_se and texture_se are redundant.,12157
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and texture_se are redundant.,12158
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and texture_se are redundant.,12159
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and texture_se are redundant.,12160
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and texture_se are redundant.,12161
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and texture_se are redundant.,12162
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and perimeter_se are redundant.,12163
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and perimeter_se are redundant.,12164
Breast_Cancer_correlation_heatmap.png,Variables texture_se and perimeter_se are redundant.,12165
Breast_Cancer_correlation_heatmap.png,Variables area_se and perimeter_se are redundant.,12166
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and perimeter_se are redundant.,12167
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_se are redundant.,12168
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and perimeter_se are redundant.,12169
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and perimeter_se are redundant.,12170
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and perimeter_se are redundant.,12171
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and area_se are redundant.,12172
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and area_se are redundant.,12173
Breast_Cancer_correlation_heatmap.png,Variables texture_se and area_se are redundant.,12174
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and area_se are redundant.,12175
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and area_se are redundant.,12176
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and area_se are redundant.,12177
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and area_se are redundant.,12178
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and area_se are redundant.,12179
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and area_se are redundant.,12180
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and smoothness_se are redundant.,12181
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and smoothness_se are redundant.,12182
Breast_Cancer_correlation_heatmap.png,Variables texture_se and smoothness_se are redundant.,12183
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and smoothness_se are redundant.,12184
Breast_Cancer_correlation_heatmap.png,Variables area_se and smoothness_se are redundant.,12185
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and smoothness_se are redundant.,12186
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and smoothness_se are redundant.,12187
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and smoothness_se are redundant.,12188
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and smoothness_se are redundant.,12189
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and symmetry_se are redundant.,12190
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and symmetry_se are redundant.,12191
Breast_Cancer_correlation_heatmap.png,Variables texture_se and symmetry_se are redundant.,12192
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and symmetry_se are redundant.,12193
Breast_Cancer_correlation_heatmap.png,Variables area_se and symmetry_se are redundant.,12194
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and symmetry_se are redundant.,12195
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and symmetry_se are redundant.,12196
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and symmetry_se are redundant.,12197
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and symmetry_se are redundant.,12198
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and radius_worst are redundant.,12199
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and radius_worst are redundant.,12200
Breast_Cancer_correlation_heatmap.png,Variables texture_se and radius_worst are redundant.,12201
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and radius_worst are redundant.,12202
Breast_Cancer_correlation_heatmap.png,Variables area_se and radius_worst are redundant.,12203
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and radius_worst are redundant.,12204
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and radius_worst are redundant.,12205
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and radius_worst are redundant.,12206
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and radius_worst are redundant.,12207
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and texture_worst are redundant.,12208
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and texture_worst are redundant.,12209
Breast_Cancer_correlation_heatmap.png,Variables texture_se and texture_worst are redundant.,12210
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and texture_worst are redundant.,12211
Breast_Cancer_correlation_heatmap.png,Variables area_se and texture_worst are redundant.,12212
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and texture_worst are redundant.,12213
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and texture_worst are redundant.,12214
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and texture_worst are redundant.,12215
Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and texture_worst are redundant.,12216
Breast_Cancer_correlation_heatmap.png,Variables texture_mean and perimeter_worst are redundant.,12217
Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and perimeter_worst are redundant.,12218
Breast_Cancer_correlation_heatmap.png,Variables texture_se and perimeter_worst are redundant.,12219
Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and perimeter_worst are redundant.,12220
Breast_Cancer_correlation_heatmap.png,Variables area_se and perimeter_worst are redundant.,12221
Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and perimeter_worst are redundant.,12222
Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_worst are redundant.,12223
Breast_Cancer_correlation_heatmap.png,Variables radius_worst and perimeter_worst are redundant.,12224
Breast_Cancer_correlation_heatmap.png,Variables texture_worst and perimeter_worst are redundant.,12225
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair texture_se and perimeter_worst.",12226
Breast_Cancer_correlation_heatmap.png,"Variables area_se and texture_worst are redundant, but we can’t say the same for the pair perimeter_se and symmetry_se.",12227
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and radius_worst are redundant, but we can’t say the same for the pair texture_se and perimeter_worst.",12228
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and area_se are redundant, but we can’t say the same for the pair smoothness_se and radius_worst.",12229
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and texture_worst are redundant, but we can’t say the same for the pair area_se and smoothness_se.",12230
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and texture_worst are redundant, but we can’t say the same for the pair texture_se and perimeter_worst.",12231
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_se.",12232
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and smoothness_se.",12233
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_worst are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_se.",12234
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and area_se.",12235
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_se.",12236
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_se and perimeter_worst.",12237
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_worst.",12238
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and smoothness_se are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_se.",12239
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and smoothness_se are redundant, but we can’t say the same for the pair texture_se and symmetry_se.",12240
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and symmetry_se.",12241
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_se are redundant, but we can’t say the same for the pair perimeter_mean and texture_worst.",12242
Breast_Cancer_correlation_heatmap.png,"Variables area_se and smoothness_se are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_se.",12243
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_se and radius_worst.",12244
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and smoothness_se are redundant, but we can’t say the same for the pair texture_se and radius_worst.",12245
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_worst.",12246
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and area_se are redundant, but we can’t say the same for the pair symmetry_se and texture_worst.",12247
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and texture_worst are redundant, but we can’t say the same for the pair area_se and symmetry_se.",12248
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_se.",12249
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and area_se.",12250
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and symmetry_se.",12251
Breast_Cancer_correlation_heatmap.png,"Variables area_se and smoothness_se are redundant, but we can’t say the same for the pair symmetry_se and radius_worst.",12252
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and area_se are redundant, but we can’t say the same for the pair perimeter_se and radius_worst.",12253
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and texture_se.",12254
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and texture_worst are redundant, but we can’t say the same for the pair texture_se and perimeter_se.",12255
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_worst are redundant, but we can’t say the same for the pair area_se and radius_worst.",12256
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and area_se are redundant, but we can’t say the same for the pair texture_mean and symmetry_se.",12257
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_worst are redundant, but we can’t say the same for the pair texture_se and perimeter_se.",12258
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and perimeter_worst are redundant, but we can’t say the same for the pair area_se and texture_worst.",12259
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_se are redundant, but we can’t say the same for the pair perimeter_mean and smoothness_se.",12260
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and perimeter_worst are redundant, but we can’t say the same for the pair area_se and symmetry_se.",12261
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_se and smoothness_se.",12262
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_se.",12263
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and texture_worst are redundant, but we can’t say the same for the pair area_se and smoothness_se.",12264
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_worst are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_worst.",12265
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair perimeter_mean and radius_worst.",12266
Breast_Cancer_correlation_heatmap.png,"Variables area_se and smoothness_se are redundant, but we can’t say the same for the pair texture_mean and perimeter_mean.",12267
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and radius_worst are redundant, but we can’t say the same for the pair perimeter_se and symmetry_se.",12268
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_worst are redundant, but we can’t say the same for the pair smoothness_se and symmetry_se.",12269
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and texture_worst.",12270
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_worst are redundant, but we can’t say the same for the pair texture_se and radius_worst.",12271
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and area_se are redundant, but we can’t say the same for the pair perimeter_mean and radius_worst.",12272
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair area_se and smoothness_se.",12273
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and symmetry_se.",12274
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and smoothness_se are redundant, but we can’t say the same for the pair perimeter_mean and area_se.",12275
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_worst are redundant, but we can’t say the same for the pair area_se and texture_worst.",12276
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and area_se are redundant, but we can’t say the same for the pair smoothness_se and symmetry_se.",12277
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_se and smoothness_se.",12278
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and symmetry_se are redundant, but we can’t say the same for the pair area_se and radius_worst.",12279
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and symmetry_se are redundant, but we can’t say the same for the pair texture_se and perimeter_worst.",12280
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and area_se are redundant, but we can’t say the same for the pair smoothness_se and symmetry_se.",12281
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_se are redundant, but we can’t say the same for the pair area_se and smoothness_se.",12282
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_worst.",12283
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_se are redundant, but we can’t say the same for the pair symmetry_se and perimeter_worst.",12284
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_mean are redundant, but we can’t say the same for the pair symmetry_se and texture_worst.",12285
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_se are redundant, but we can’t say the same for the pair area_se and texture_worst.",12286
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and area_se are redundant, but we can’t say the same for the pair texture_se and texture_worst.",12287
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12288
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and texture_se are redundant, but we can’t say the same for the pair texture_worst and perimeter_worst.",12289
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and smoothness_se are redundant, but we can’t say the same for the pair area_se and radius_worst.",12290
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_mean are redundant, but we can’t say the same for the pair texture_se and area_se.",12291
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and radius_worst are redundant, but we can’t say the same for the pair texture_se and area_se.",12292
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_worst are redundant, but we can’t say the same for the pair area_se and symmetry_se.",12293
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and area_se are redundant, but we can’t say the same for the pair radius_worst and perimeter_worst.",12294
Breast_Cancer_correlation_heatmap.png,"Variables area_se and symmetry_se are redundant, but we can’t say the same for the pair texture_se and smoothness_se.",12295
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair smoothness_se and texture_worst.",12296
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and smoothness_se are redundant, but we can’t say the same for the pair symmetry_se and perimeter_worst.",12297
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_se are redundant, but we can’t say the same for the pair texture_worst and perimeter_worst.",12298
Breast_Cancer_correlation_heatmap.png,"Variables area_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and texture_se.",12299
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and texture_worst are redundant, but we can’t say the same for the pair area_se and perimeter_worst.",12300
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and perimeter_worst are redundant, but we can’t say the same for the pair smoothness_se and symmetry_se.",12301
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_worst.",12302
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and texture_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_se.",12303
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair smoothness_se and radius_worst.",12304
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and texture_worst are redundant, but we can’t say the same for the pair smoothness_se and symmetry_se.",12305
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and area_se are redundant, but we can’t say the same for the pair smoothness_se and symmetry_se.",12306
Breast_Cancer_correlation_heatmap.png,"Variables area_se and perimeter_worst are redundant, but we can’t say the same for the pair smoothness_se and texture_worst.",12307
Breast_Cancer_correlation_heatmap.png,"Variables area_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12308
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and smoothness_se are redundant, but we can’t say the same for the pair perimeter_se and area_se.",12309
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and texture_worst are redundant, but we can’t say the same for the pair perimeter_se and perimeter_worst.",12310
Breast_Cancer_correlation_heatmap.png,"Variables texture_worst and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and radius_worst.",12311
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and texture_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_se.",12312
Breast_Cancer_correlation_heatmap.png,"Variables texture_worst and perimeter_worst are redundant, but we can’t say the same for the pair texture_se and perimeter_se.",12313
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_se are redundant, but we can’t say the same for the pair texture_se and symmetry_se.",12314
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and area_se are redundant, but we can’t say the same for the pair perimeter_se and perimeter_worst.",12315
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and smoothness_se are redundant, but we can’t say the same for the pair texture_worst and perimeter_worst.",12316
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and texture_worst are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_worst.",12317
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and texture_se.",12318
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_mean and area_se.",12319
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and texture_worst are redundant, but we can’t say the same for the pair smoothness_se and perimeter_worst.",12320
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_se and symmetry_se.",12321
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and radius_worst.",12322
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_se are redundant, but we can’t say the same for the pair smoothness_se and radius_worst.",12323
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and radius_worst are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12324
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and area_se are redundant, but we can’t say the same for the pair radius_worst and texture_worst.",12325
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and texture_worst are redundant, but we can’t say the same for the pair symmetry_se and perimeter_worst.",12326
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12327
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and area_se.",12328
Breast_Cancer_correlation_heatmap.png,"Variables area_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_se.",12329
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and radius_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_se.",12330
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and perimeter_worst are redundant, but we can’t say the same for the pair texture_se and texture_worst.",12331
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_se and radius_worst.",12332
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair texture_se and smoothness_se.",12333
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12334
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and area_se are redundant, but we can’t say the same for the pair symmetry_se and texture_worst.",12335
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and texture_worst are redundant, but we can’t say the same for the pair perimeter_se and area_se.",12336
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_se and area_se.",12337
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and perimeter_worst.",12338
Breast_Cancer_correlation_heatmap.png,"Variables area_se and texture_worst are redundant, but we can’t say the same for the pair perimeter_mean and smoothness_se.",12339
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12340
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_worst.",12341
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and area_se are redundant, but we can’t say the same for the pair perimeter_mean and texture_worst.",12342
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and symmetry_se are redundant, but we can’t say the same for the pair texture_se and perimeter_se.",12343
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and radius_worst are redundant, but we can’t say the same for the pair area_se and smoothness_se.",12344
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and perimeter_mean.",12345
Breast_Cancer_correlation_heatmap.png,"Variables area_se and smoothness_se are redundant, but we can’t say the same for the pair perimeter_se and texture_worst.",12346
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_mean are redundant, but we can’t say the same for the pair area_se and perimeter_worst.",12347
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and radius_worst are redundant, but we can’t say the same for the pair perimeter_se and area_se.",12348
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and symmetry_se are redundant, but we can’t say the same for the pair perimeter_se and radius_worst.",12349
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_mean.",12350
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and texture_worst are redundant, but we can’t say the same for the pair texture_se and radius_worst.",12351
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_worst are redundant, but we can’t say the same for the pair perimeter_mean and symmetry_se.",12352
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_se and radius_worst.",12353
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and texture_worst are redundant, but we can’t say the same for the pair texture_mean and symmetry_se.",12354
Breast_Cancer_correlation_heatmap.png,"Variables area_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_worst.",12355
Breast_Cancer_correlation_heatmap.png,"Variables radius_worst and texture_worst are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_se.",12356
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and texture_worst are redundant, but we can’t say the same for the pair texture_se and radius_worst.",12357
Breast_Cancer_correlation_heatmap.png,"Variables area_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and smoothness_se.",12358
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and texture_worst are redundant, but we can’t say the same for the pair perimeter_se and radius_worst.",12359
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and texture_worst are redundant, but we can’t say the same for the pair texture_se and perimeter_worst.",12360
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_se are redundant, but we can’t say the same for the pair smoothness_se and perimeter_worst.",12361
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_worst are redundant, but we can’t say the same for the pair symmetry_se and radius_worst.",12362
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and symmetry_se are redundant, but we can’t say the same for the pair perimeter_se and smoothness_se.",12363
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and texture_se are redundant, but we can’t say the same for the pair area_se and texture_worst.",12364
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and radius_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_se.",12365
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and area_se are redundant, but we can’t say the same for the pair texture_mean and texture_worst.",12366
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and area_se are redundant, but we can’t say the same for the pair texture_se and smoothness_se.",12367
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and area_se.",12368
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_se are redundant, but we can’t say the same for the pair symmetry_se and radius_worst.",12369
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_worst are redundant, but we can’t say the same for the pair area_se and radius_worst.",12370
Breast_Cancer_correlation_heatmap.png,"Variables area_se and radius_worst are redundant, but we can’t say the same for the pair smoothness_se and perimeter_worst.",12371
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and perimeter_worst are redundant, but we can’t say the same for the pair texture_mean and texture_worst.",12372
Breast_Cancer_correlation_heatmap.png,"Variables area_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_se.",12373
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and symmetry_se.",12374
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_mean are redundant, but we can’t say the same for the pair perimeter_se and symmetry_se.",12375
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and smoothness_se are redundant, but we can’t say the same for the pair perimeter_se and texture_worst.",12376
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and radius_worst are redundant, but we can’t say the same for the pair texture_mean and symmetry_se.",12377
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair area_se and radius_worst.",12378
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and radius_worst are redundant, but we can’t say the same for the pair texture_se and smoothness_se.",12379
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_se and smoothness_se.",12380
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and symmetry_se are redundant, but we can’t say the same for the pair radius_worst and perimeter_worst.",12381
Breast_Cancer_correlation_heatmap.png,"Variables area_se and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12382
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_worst.",12383
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_se are redundant, but we can’t say the same for the pair texture_se and area_se.",12384
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and texture_worst are redundant, but we can’t say the same for the pair texture_se and perimeter_se.",12385
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_se and perimeter_worst.",12386
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_mean are redundant, but we can’t say the same for the pair smoothness_se and symmetry_se.",12387
Breast_Cancer_correlation_heatmap.png,"Variables area_se and smoothness_se are redundant, but we can’t say the same for the pair texture_mean and perimeter_worst.",12388
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and area_se are redundant, but we can’t say the same for the pair perimeter_mean and symmetry_se.",12389
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and area_se are redundant, but we can’t say the same for the pair perimeter_se and smoothness_se.",12390
Breast_Cancer_correlation_heatmap.png,"Variables texture_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and area_se.",12391
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and texture_worst are redundant, but we can’t say the same for the pair area_se and perimeter_worst.",12392
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_se and area_se.",12393
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and area_se are redundant, but we can’t say the same for the pair texture_se and texture_worst.",12394
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_se are redundant, but we can’t say the same for the pair area_se and smoothness_se.",12395
Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and radius_worst are redundant, but we can’t say the same for the pair texture_mean and perimeter_mean.",12396
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_worst are redundant, but we can’t say the same for the pair symmetry_se and texture_worst.",12397
Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and texture_worst are redundant, but we can’t say the same for the pair texture_mean and area_se.",12398
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and perimeter_worst are redundant, but we can’t say the same for the pair area_se and radius_worst.",12399
Breast_Cancer_correlation_heatmap.png,"Variables area_se and texture_worst are redundant, but we can’t say the same for the pair smoothness_se and radius_worst.",12400
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and area_se are redundant, but we can’t say the same for the pair smoothness_se and texture_worst.",12401
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and smoothness_se are redundant, but we can’t say the same for the pair texture_se and perimeter_worst.",12402
Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_worst are redundant, but we can’t say the same for the pair perimeter_se and perimeter_worst.",12403
Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_mean and texture_se.",12404
Breast_Cancer_correlation_heatmap.png,"Variables area_se and smoothness_se are redundant, but we can’t say the same for the pair perimeter_se and symmetry_se.",12405
Breast_Cancer_correlation_heatmap.png,The variable texture_mean can be discarded without risking losing information.,12406
Breast_Cancer_correlation_heatmap.png,The variable perimeter_mean can be discarded without risking losing information.,12407
Breast_Cancer_correlation_heatmap.png,The variable texture_se can be discarded without risking losing information.,12408
Breast_Cancer_correlation_heatmap.png,The variable perimeter_se can be discarded without risking losing information.,12409
Breast_Cancer_correlation_heatmap.png,The variable area_se can be discarded without risking losing information.,12410
Breast_Cancer_correlation_heatmap.png,The variable smoothness_se can be discarded without risking losing information.,12411
Breast_Cancer_correlation_heatmap.png,The variable symmetry_se can be discarded without risking losing information.,12412
Breast_Cancer_correlation_heatmap.png,The variable radius_worst can be discarded without risking losing information.,12413
Breast_Cancer_correlation_heatmap.png,The variable texture_worst can be discarded without risking losing information.,12414
Breast_Cancer_correlation_heatmap.png,The variable perimeter_worst can be discarded without risking losing information.,12415
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or texture_mean can be discarded without losing information.,12416
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or texture_mean can be discarded without losing information.,12417
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or texture_mean can be discarded without losing information.,12418
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or texture_mean can be discarded without losing information.,12419
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or texture_mean can be discarded without losing information.,12420
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or texture_mean can be discarded without losing information.,12421
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or texture_mean can be discarded without losing information.,12422
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or texture_mean can be discarded without losing information.,12423
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or texture_mean can be discarded without losing information.,12424
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or perimeter_mean can be discarded without losing information.,12425
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or perimeter_mean can be discarded without losing information.,12426
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or perimeter_mean can be discarded without losing information.,12427
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or perimeter_mean can be discarded without losing information.,12428
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or perimeter_mean can be discarded without losing information.,12429
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or perimeter_mean can be discarded without losing information.,12430
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or perimeter_mean can be discarded without losing information.,12431
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or perimeter_mean can be discarded without losing information.,12432
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or perimeter_mean can be discarded without losing information.,12433
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or texture_se can be discarded without losing information.,12434
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or texture_se can be discarded without losing information.,12435
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or texture_se can be discarded without losing information.,12436
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or texture_se can be discarded without losing information.,12437
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or texture_se can be discarded without losing information.,12438
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or texture_se can be discarded without losing information.,12439
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or texture_se can be discarded without losing information.,12440
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or texture_se can be discarded without losing information.,12441
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or texture_se can be discarded without losing information.,12442
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or perimeter_se can be discarded without losing information.,12443
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or perimeter_se can be discarded without losing information.,12444
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or perimeter_se can be discarded without losing information.,12445
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or perimeter_se can be discarded without losing information.,12446
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or perimeter_se can be discarded without losing information.,12447
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or perimeter_se can be discarded without losing information.,12448
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or perimeter_se can be discarded without losing information.,12449
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or perimeter_se can be discarded without losing information.,12450
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or perimeter_se can be discarded without losing information.,12451
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or area_se can be discarded without losing information.,12452
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or area_se can be discarded without losing information.,12453
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or area_se can be discarded without losing information.,12454
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or area_se can be discarded without losing information.,12455
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or area_se can be discarded without losing information.,12456
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or area_se can be discarded without losing information.,12457
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or area_se can be discarded without losing information.,12458
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or area_se can be discarded without losing information.,12459
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or area_se can be discarded without losing information.,12460
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or smoothness_se can be discarded without losing information.,12461
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or smoothness_se can be discarded without losing information.,12462
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or smoothness_se can be discarded without losing information.,12463
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or smoothness_se can be discarded without losing information.,12464
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or smoothness_se can be discarded without losing information.,12465
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or smoothness_se can be discarded without losing information.,12466
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or smoothness_se can be discarded without losing information.,12467
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or smoothness_se can be discarded without losing information.,12468
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or smoothness_se can be discarded without losing information.,12469
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or symmetry_se can be discarded without losing information.,12470
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or symmetry_se can be discarded without losing information.,12471
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or symmetry_se can be discarded without losing information.,12472
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or symmetry_se can be discarded without losing information.,12473
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or symmetry_se can be discarded without losing information.,12474
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or symmetry_se can be discarded without losing information.,12475
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or symmetry_se can be discarded without losing information.,12476
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or symmetry_se can be discarded without losing information.,12477
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or symmetry_se can be discarded without losing information.,12478
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or radius_worst can be discarded without losing information.,12479
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or radius_worst can be discarded without losing information.,12480
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or radius_worst can be discarded without losing information.,12481
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or radius_worst can be discarded without losing information.,12482
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or radius_worst can be discarded without losing information.,12483
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or radius_worst can be discarded without losing information.,12484
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or radius_worst can be discarded without losing information.,12485
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or radius_worst can be discarded without losing information.,12486
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or radius_worst can be discarded without losing information.,12487
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or texture_worst can be discarded without losing information.,12488
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or texture_worst can be discarded without losing information.,12489
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or texture_worst can be discarded without losing information.,12490
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or texture_worst can be discarded without losing information.,12491
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or texture_worst can be discarded without losing information.,12492
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or texture_worst can be discarded without losing information.,12493
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or texture_worst can be discarded without losing information.,12494
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or texture_worst can be discarded without losing information.,12495
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or texture_worst can be discarded without losing information.,12496
Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or perimeter_worst can be discarded without losing information.,12497
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or perimeter_worst can be discarded without losing information.,12498
Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or perimeter_worst can be discarded without losing information.,12499
Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or perimeter_worst can be discarded without losing information.,12500
Breast_Cancer_correlation_heatmap.png,One of the variables area_se or perimeter_worst can be discarded without losing information.,12501
Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or perimeter_worst can be discarded without losing information.,12502
Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or perimeter_worst can be discarded without losing information.,12503
Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or perimeter_worst can be discarded without losing information.,12504
Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or perimeter_worst can be discarded without losing information.,12505
Breast_Cancer_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,12506
Breast_Cancer_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,12507
Breast_Cancer_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,12508
Breast_Cancer_boxplots.png,At least 50 of the variables present outliers.,12509
Breast_Cancer_histograms_numeric.png,At least 60 of the variables present outliers.,12510
Breast_Cancer_histograms_numeric.png,At least 75 of the variables present outliers.,12511
Breast_Cancer_boxplots.png,At least 85 of the variables present outliers.,12512
Breast_Cancer_boxplots.png,Variable texture_mean presents some outliers.,12513
Breast_Cancer_boxplots.png,Variable perimeter_mean presents some outliers.,12514
Breast_Cancer_boxplots.png,Variable texture_se presents some outliers.,12515
Breast_Cancer_boxplots.png,Variable perimeter_se presents some outliers.,12516
Breast_Cancer_boxplots.png,Variable area_se presents some outliers.,12517
Breast_Cancer_histograms_numeric.png,Variable smoothness_se presents some outliers.,12518
Breast_Cancer_histograms_numeric.png,Variable symmetry_se presents some outliers.,12519
Breast_Cancer_histograms_numeric.png,Variable radius_worst presents some outliers.,12520
Breast_Cancer_boxplots.png,Variable texture_worst presents some outliers.,12521
Breast_Cancer_boxplots.png,Variable perimeter_worst presents some outliers.,12522
Breast_Cancer_histograms_numeric.png,Variable texture_mean doesn’t have any outliers.,12523
Breast_Cancer_boxplots.png,Variable perimeter_mean doesn’t have any outliers.,12524
Breast_Cancer_boxplots.png,Variable texture_se doesn’t have any outliers.,12525
Breast_Cancer_boxplots.png,Variable perimeter_se doesn’t have any outliers.,12526
Breast_Cancer_histograms_numeric.png,Variable area_se doesn’t have any outliers.,12527
Breast_Cancer_boxplots.png,Variable smoothness_se doesn’t have any outliers.,12528
Breast_Cancer_boxplots.png,Variable symmetry_se doesn’t have any outliers.,12529
Breast_Cancer_boxplots.png,Variable radius_worst doesn’t have any outliers.,12530
Breast_Cancer_boxplots.png,Variable texture_worst doesn’t have any outliers.,12531
Breast_Cancer_boxplots.png,Variable perimeter_worst doesn’t have any outliers.,12532
Breast_Cancer_boxplots.png,Variable texture_mean shows some outlier values.,12533
Breast_Cancer_histograms_numeric.png,Variable perimeter_mean shows some outlier values.,12534
Breast_Cancer_histograms_numeric.png,Variable texture_se shows some outlier values.,12535
Breast_Cancer_boxplots.png,Variable perimeter_se shows some outlier values.,12536
Breast_Cancer_histograms_numeric.png,Variable area_se shows some outlier values.,12537
Breast_Cancer_boxplots.png,Variable smoothness_se shows some outlier values.,12538
Breast_Cancer_histograms_numeric.png,Variable symmetry_se shows some outlier values.,12539
Breast_Cancer_boxplots.png,Variable radius_worst shows some outlier values.,12540
Breast_Cancer_boxplots.png,Variable texture_worst shows some outlier values.,12541
Breast_Cancer_boxplots.png,Variable perimeter_worst shows some outlier values.,12542
Breast_Cancer_histograms_numeric.png,Variable texture_mean shows a high number of outlier values.,12543
Breast_Cancer_histograms_numeric.png,Variable perimeter_mean shows a high number of outlier values.,12544
Breast_Cancer_histograms_numeric.png,Variable texture_se shows a high number of outlier values.,12545
Breast_Cancer_histograms_numeric.png,Variable perimeter_se shows a high number of outlier values.,12546
Breast_Cancer_boxplots.png,Variable area_se shows a high number of outlier values.,12547
Breast_Cancer_histograms_numeric.png,Variable smoothness_se shows a high number of outlier values.,12548
Breast_Cancer_boxplots.png,Variable symmetry_se shows a high number of outlier values.,12549
Breast_Cancer_boxplots.png,Variable radius_worst shows a high number of outlier values.,12550
Breast_Cancer_histograms_numeric.png,Variable texture_worst shows a high number of outlier values.,12551
Breast_Cancer_boxplots.png,Variable perimeter_worst shows a high number of outlier values.,12552
Breast_Cancer_histograms_numeric.png,Outliers seem to be a problem in the dataset.,12553
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable texture_mean.",12554
Breast_Cancer_boxplots.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable texture_mean.",12555
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable texture_mean.",12556
Breast_Cancer_boxplots.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable texture_mean.",12557
Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable texture_mean.",12558
Breast_Cancer_boxplots.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable texture_mean.",12559
Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable texture_mean.",12560
Breast_Cancer_boxplots.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable texture_mean.",12561
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable texture_mean.",12562
Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12563
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12564
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12565
Breast_Cancer_boxplots.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12566
Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12567
Breast_Cancer_histograms_numeric.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12568
Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12569
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12570
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12571
Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable texture_se.",12572
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable texture_se.",12573
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable texture_se.",12574
Breast_Cancer_histograms_numeric.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable texture_se.",12575
Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable texture_se.",12576
Breast_Cancer_histograms_numeric.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable texture_se.",12577
Breast_Cancer_boxplots.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable texture_se.",12578
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable texture_se.",12579
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable texture_se.",12580
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12581
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12582
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12583
Breast_Cancer_boxplots.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12584
Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12585
Breast_Cancer_histograms_numeric.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12586
Breast_Cancer_boxplots.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12587
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12588
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12589
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable area_se.",12590
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable area_se.",12591
Breast_Cancer_boxplots.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable area_se.",12592
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable area_se.",12593
Breast_Cancer_histograms_numeric.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable area_se.",12594
Breast_Cancer_histograms_numeric.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable area_se.",12595
Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable area_se.",12596
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable area_se.",12597
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable area_se.",12598
Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12599
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12600
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12601
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12602
Breast_Cancer_boxplots.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12603
Breast_Cancer_boxplots.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12604
Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12605
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12606
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12607
Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12608
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12609
Breast_Cancer_boxplots.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12610
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12611
Breast_Cancer_histograms_numeric.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12612
Breast_Cancer_histograms_numeric.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12613
Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12614
Breast_Cancer_boxplots.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12615
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12616
Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable radius_worst.",12617
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable radius_worst.",12618
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable radius_worst.",12619
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable radius_worst.",12620
Breast_Cancer_boxplots.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable radius_worst.",12621
Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable radius_worst.",12622
Breast_Cancer_boxplots.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable radius_worst.",12623
Breast_Cancer_boxplots.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable radius_worst.",12624
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable radius_worst.",12625
Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable texture_worst.",12626
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable texture_worst.",12627
Breast_Cancer_boxplots.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable texture_worst.",12628
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable texture_worst.",12629
Breast_Cancer_boxplots.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable texture_worst.",12630
Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable texture_worst.",12631
Breast_Cancer_histograms_numeric.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable texture_worst.",12632
Breast_Cancer_boxplots.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable texture_worst.",12633
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable texture_worst.",12634
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12635
Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12636
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12637
Breast_Cancer_boxplots.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12638
Breast_Cancer_boxplots.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12639
Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12640
Breast_Cancer_boxplots.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12641
Breast_Cancer_boxplots.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12642
Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12643
Breast_Cancer_boxplots.png,Those boxplots show that the data is not normalized.,12644
Breast_Cancer_boxplots.png,Variable texture_mean is balanced.,12645
Breast_Cancer_boxplots.png,Variable perimeter_mean is balanced.,12646
Breast_Cancer_histograms_numeric.png,Variable texture_se is balanced.,12647
Breast_Cancer_histograms_numeric.png,Variable perimeter_se is balanced.,12648
Breast_Cancer_boxplots.png,Variable area_se is balanced.,12649
Breast_Cancer_boxplots.png,Variable smoothness_se is balanced.,12650
Breast_Cancer_histograms_numeric.png,Variable symmetry_se is balanced.,12651
Breast_Cancer_histograms_numeric.png,Variable radius_worst is balanced.,12652
Breast_Cancer_histograms_numeric.png,Variable texture_worst is balanced.,12653
Breast_Cancer_histograms_numeric.png,Variable perimeter_worst is balanced.,12654
Breast_Cancer_histograms.png,The variable texture_mean can be seen as ordinal without losing information.,12655
Breast_Cancer_histograms.png,The variable perimeter_mean can be seen as ordinal without losing information.,12656
Breast_Cancer_histograms.png,The variable texture_se can be seen as ordinal without losing information.,12657
Breast_Cancer_histograms.png,The variable perimeter_se can be seen as ordinal without losing information.,12658
Breast_Cancer_histograms.png,The variable area_se can be seen as ordinal without losing information.,12659
Breast_Cancer_histograms.png,The variable smoothness_se can be seen as ordinal without losing information.,12660
Breast_Cancer_histograms.png,The variable symmetry_se can be seen as ordinal without losing information.,12661
Breast_Cancer_histograms.png,The variable radius_worst can be seen as ordinal without losing information.,12662
Breast_Cancer_histograms.png,The variable texture_worst can be seen as ordinal without losing information.,12663
Breast_Cancer_histograms.png,The variable perimeter_worst can be seen as ordinal without losing information.,12664
Breast_Cancer_histograms.png,The variable texture_mean can be seen as ordinal.,12665
Breast_Cancer_histograms.png,The variable perimeter_mean can be seen as ordinal.,12666
Breast_Cancer_histograms.png,The variable texture_se can be seen as ordinal.,12667
Breast_Cancer_histograms.png,The variable perimeter_se can be seen as ordinal.,12668
Breast_Cancer_histograms.png,The variable area_se can be seen as ordinal.,12669
Breast_Cancer_histograms.png,The variable smoothness_se can be seen as ordinal.,12670
Breast_Cancer_histograms.png,The variable symmetry_se can be seen as ordinal.,12671
Breast_Cancer_histograms.png,The variable radius_worst can be seen as ordinal.,12672
Breast_Cancer_histograms.png,The variable texture_worst can be seen as ordinal.,12673
Breast_Cancer_histograms.png,The variable perimeter_worst can be seen as ordinal.,12674
Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as numeric.",12675
Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as binary.",12676
Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as date.",12677
Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as symbolic.",12678
Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 68.,12679
Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 23.,12680
Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 67.,12681
Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 60.,12682
Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 95.,12683
Breast_Cancer_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,12684
Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",12685
Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",12686
Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",12687
Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",12688
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A,B), as 5.",12689
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 5.",12690
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 5.",12691
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 5.",12692
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A,B), as 6.",12693
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 6.",12694
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 6.",12695
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 6.",12696
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A,B), as 7.",12697
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 7.",12698
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 7.",12699
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 7.",12700
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A,B), as 4.",12701
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 4.",12702
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 4.",12703
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 4.",12704
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A,B), as 8.",12705
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 8.",12706
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 8.",12707
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 8.",12708
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A,B), as 3.",12709
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 3.",12710
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 3.",12711
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 3.",12712
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 5 for any k ≤ 154.",12713
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 5 for any k ≤ 154.",12714
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 5 for any k ≤ 154.",12715
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 5 for any k ≤ 154.",12716
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 6 for any k ≤ 154.",12717
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 6 for any k ≤ 154.",12718
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 6 for any k ≤ 154.",12719
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 6 for any k ≤ 154.",12720
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 7 for any k ≤ 154.",12721
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 7 for any k ≤ 154.",12722
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 7 for any k ≤ 154.",12723
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 7 for any k ≤ 154.",12724
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 154.",12725
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 154.",12726
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 154.",12727
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 154.",12728
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 8 for any k ≤ 154.",12729
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 8 for any k ≤ 154.",12730
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 8 for any k ≤ 154.",12731
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 8 for any k ≤ 154.",12732
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 154.",12733
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 154.",12734
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 154.",12735
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 154.",12736
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 5 for any k ≤ 27.",12737
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 5 for any k ≤ 27.",12738
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 5 for any k ≤ 27.",12739
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 5 for any k ≤ 27.",12740
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 6 for any k ≤ 27.",12741
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 6 for any k ≤ 27.",12742
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 6 for any k ≤ 27.",12743
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 6 for any k ≤ 27.",12744
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 7 for any k ≤ 27.",12745
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 7 for any k ≤ 27.",12746
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 7 for any k ≤ 27.",12747
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 7 for any k ≤ 27.",12748
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 27.",12749
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 27.",12750
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 27.",12751
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 27.",12752
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 8 for any k ≤ 27.",12753
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 8 for any k ≤ 27.",12754
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 8 for any k ≤ 27.",12755
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 8 for any k ≤ 27.",12756
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 27.",12757
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 27.",12758
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 27.",12759
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 27.",12760
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 5 for any k ≤ 172.",12761
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 5 for any k ≤ 172.",12762
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 5 for any k ≤ 172.",12763
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 5 for any k ≤ 172.",12764
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 6 for any k ≤ 172.",12765
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 6 for any k ≤ 172.",12766
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 6 for any k ≤ 172.",12767
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 6 for any k ≤ 172.",12768
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 7 for any k ≤ 172.",12769
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 7 for any k ≤ 172.",12770
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 7 for any k ≤ 172.",12771
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 7 for any k ≤ 172.",12772
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 172.",12773
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 172.",12774
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 172.",12775
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 172.",12776
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 8 for any k ≤ 172.",12777
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 8 for any k ≤ 172.",12778
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 8 for any k ≤ 172.",12779
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 8 for any k ≤ 172.",12780
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 172.",12781
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 172.",12782
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 172.",12783
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 172.",12784
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 5 for any k ≤ 447.",12785
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 5 for any k ≤ 447.",12786
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 5 for any k ≤ 447.",12787
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 5 for any k ≤ 447.",12788
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 6 for any k ≤ 447.",12789
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 6 for any k ≤ 447.",12790
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 6 for any k ≤ 447.",12791
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 6 for any k ≤ 447.",12792
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 7 for any k ≤ 447.",12793
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 7 for any k ≤ 447.",12794
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 7 for any k ≤ 447.",12795
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 7 for any k ≤ 447.",12796
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 447.",12797
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 447.",12798
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 447.",12799
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 447.",12800
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 8 for any k ≤ 447.",12801
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 8 for any k ≤ 447.",12802
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 8 for any k ≤ 447.",12803
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 8 for any k ≤ 447.",12804
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 447.",12805
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 447.",12806
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 447.",12807
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 447.",12808
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A,B) as 5.",12809
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, B) as 5.",12810
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A, not B) as 5.",12811
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, not B) as 5.",12812
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A,B) as 6.",12813
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, B) as 6.",12814
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A, not B) as 6.",12815
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, not B) as 6.",12816
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A,B) as 7.",12817
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, B) as 7.",12818
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A, not B) as 7.",12819
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, not B) as 7.",12820
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A,B) as 4.",12821
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, B) as 4.",12822
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A, not B) as 4.",12823
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, not B) as 4.",12824
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A,B) as 8.",12825
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, B) as 8.",12826
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A, not B) as 8.",12827
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, not B) as 8.",12828
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A,B) as 3.",12829
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, B) as 3.",12830
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (A, not B) as 3.",12831
WineQT_decision_tree.png,"Considering that A=True<=>density<=1.0 and B=True<=>chlorides<=0.08, the Decision Tree presented classifies (not A, not B) as 3.",12832
WineQT_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1046 episodes.,12833
WineQT_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1177 episodes.,12834
WineQT_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1015 episodes.,12835
WineQT_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 706 episodes.,12836
WineQT_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1307 episodes.,12837
WineQT_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 64 estimators.,12838
WineQT_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 87 estimators.,12839
WineQT_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 148 estimators.,12840
WineQT_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 146 estimators.,12841
WineQT_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 92 estimators.,12842
WineQT_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 100 estimators.,12843
WineQT_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 67 estimators.,12844
WineQT_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 136 estimators.,12845
WineQT_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 93 estimators.,12846
WineQT_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 95 estimators.,12847
WineQT_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,12848
WineQT_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,12849
WineQT_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,12850
WineQT_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,12851
WineQT_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,12852
WineQT_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,12853
WineQT_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,12854
WineQT_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,12855
WineQT_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,12856
WineQT_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,12857
WineQT_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,12858
WineQT_decision_tree.png,The recall for the presented tree is higher than its accuracy.,12859
WineQT_decision_tree.png,The precision for the presented tree is higher than its accuracy.,12860
WineQT_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,12861
WineQT_decision_tree.png,The recall for the presented tree is lower than its accuracy.,12862
WineQT_decision_tree.png,The precision for the presented tree is lower than its accuracy.,12863
WineQT_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,12864
WineQT_decision_tree.png,The accuracy for the presented tree is higher than its recall.,12865
WineQT_decision_tree.png,The precision for the presented tree is higher than its recall.,12866
WineQT_decision_tree.png,The specificity for the presented tree is higher than its recall.,12867
WineQT_decision_tree.png,The accuracy for the presented tree is lower than its recall.,12868
WineQT_decision_tree.png,The precision for the presented tree is lower than its recall.,12869
WineQT_decision_tree.png,The specificity for the presented tree is lower than its recall.,12870
WineQT_decision_tree.png,The accuracy for the presented tree is higher than its precision.,12871
WineQT_decision_tree.png,The recall for the presented tree is higher than its precision.,12872
WineQT_decision_tree.png,The specificity for the presented tree is higher than its precision.,12873
WineQT_decision_tree.png,The accuracy for the presented tree is lower than its precision.,12874
WineQT_decision_tree.png,The recall for the presented tree is lower than its precision.,12875
WineQT_decision_tree.png,The specificity for the presented tree is lower than its precision.,12876
WineQT_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,12877
WineQT_decision_tree.png,The recall for the presented tree is higher than its specificity.,12878
WineQT_decision_tree.png,The precision for the presented tree is higher than its specificity.,12879
WineQT_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,12880
WineQT_decision_tree.png,The recall for the presented tree is lower than its specificity.,12881
WineQT_decision_tree.png,The precision for the presented tree is lower than its specificity.,12882
WineQT_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,12883
WineQT_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,12884
WineQT_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,12885
WineQT_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,12886
WineQT_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,12887
WineQT_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,12888
WineQT_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,12889
WineQT_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,12890
WineQT_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,12891
WineQT_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,12892
WineQT_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,12893
WineQT_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,12894
WineQT_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,12895
WineQT_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,12896
WineQT_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,12897
WineQT_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,12898
WineQT_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,12899
WineQT_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,12900
WineQT_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,12901
WineQT_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,12902
WineQT_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,12903
WineQT_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,12904
WineQT_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,12905
WineQT_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,12906
WineQT_decision_tree.png,The number of True Positives reported in the same tree is 33.,12907
WineQT_decision_tree.png,The number of False Positives reported in the same tree is 30.,12908
WineQT_decision_tree.png,The number of True Negatives reported in the same tree is 47.,12909
WineQT_decision_tree.png,The number of False Negatives reported in the same tree is 26.,12910
WineQT_decision_tree.png,The number of True Positives reported in the same tree is 16.,12911
WineQT_decision_tree.png,The number of False Positives reported in the same tree is 24.,12912
WineQT_decision_tree.png,The number of True Negatives reported in the same tree is 14.,12913
WineQT_decision_tree.png,The number of False Negatives reported in the same tree is 42.,12914
WineQT_decision_tree.png,The number of True Positives reported in the same tree is 32.,12915
WineQT_decision_tree.png,The number of False Positives reported in the same tree is 17.,12916
WineQT_decision_tree.png,The number of True Negatives reported in the same tree is 43.,12917
WineQT_decision_tree.png,The number of False Negatives reported in the same tree is 18.,12918
WineQT_decision_tree.png,The number of True Positives reported in the same tree is 50.,12919
WineQT_decision_tree.png,The number of False Positives reported in the same tree is 20.,12920
WineQT_decision_tree.png,The number of True Negatives reported in the same tree is 13.,12921
WineQT_decision_tree.png,The number of False Negatives reported in the same tree is 48.,12922
WineQT_decision_tree.png,The number of True Positives reported in the same tree is 27.,12923
WineQT_decision_tree.png,The number of False Positives reported in the same tree is 45.,12924
WineQT_decision_tree.png,The number of True Negatives reported in the same tree is 11.,12925
WineQT_decision_tree.png,The number of False Negatives reported in the same tree is 23.,12926
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,12927
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,12928
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,12929
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,12930
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,12931
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,12932
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,12933
WineQT_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,12934
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,12935
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,12936
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,12937
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,12938
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,12939
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,12940
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,12941
WineQT_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,12942
WineQT_decision_tree.png,The accuracy for the presented tree is higher than 75%.,12943
WineQT_decision_tree.png,The recall for the presented tree is higher than 85%.,12944
WineQT_decision_tree.png,The precision for the presented tree is higher than 66%.,12945
WineQT_decision_tree.png,The specificity for the presented tree is higher than 90%.,12946
WineQT_decision_tree.png,The accuracy for the presented tree is lower than 60%.,12947
WineQT_decision_tree.png,The recall for the presented tree is lower than 71%.,12948
WineQT_decision_tree.png,The precision for the presented tree is lower than 72%.,12949
WineQT_decision_tree.png,The specificity for the presented tree is lower than 88%.,12950
WineQT_decision_tree.png,The accuracy for the presented tree is higher than 68%.,12951
WineQT_decision_tree.png,The recall for the presented tree is higher than 73%.,12952
WineQT_decision_tree.png,The precision for the presented tree is higher than 83%.,12953
WineQT_decision_tree.png,The specificity for the presented tree is higher than 87%.,12954
WineQT_decision_tree.png,The accuracy for the presented tree is lower than 64%.,12955
WineQT_decision_tree.png,The recall for the presented tree is lower than 82%.,12956
WineQT_decision_tree.png,The precision for the presented tree is lower than 69%.,12957
WineQT_decision_tree.png,The specificity for the presented tree is lower than 70%.,12958
WineQT_decision_tree.png,The accuracy for the presented tree is higher than 74%.,12959
WineQT_decision_tree.png,The recall for the presented tree is higher than 81%.,12960
WineQT_decision_tree.png,The precision for the presented tree is higher than 89%.,12961
WineQT_decision_tree.png,The specificity for the presented tree is higher than 62%.,12962
WineQT_decision_tree.png,The accuracy for the presented tree is lower than 86%.,12963
WineQT_decision_tree.png,The recall for the presented tree is lower than 79%.,12964
WineQT_decision_tree.png,The precision for the presented tree is lower than 76%.,12965
WineQT_decision_tree.png,The specificity for the presented tree is lower than 80%.,12966
WineQT_decision_tree.png,The accuracy for the presented tree is higher than 63%.,12967
WineQT_decision_tree.png,The recall for the presented tree is higher than 84%.,12968
WineQT_decision_tree.png,The precision for the presented tree is higher than 77%.,12969
WineQT_decision_tree.png,The specificity for the presented tree is higher than 67%.,12970
WineQT_decision_tree.png,The accuracy for the presented tree is lower than 78%.,12971
WineQT_decision_tree.png,The recall for the presented tree is lower than 61%.,12972
WineQT_decision_tree.png,The precision for the presented tree is lower than 65%.,12973
WineQT_decision_tree.png,The specificity for the presented tree is lower than 75%.,12974
WineQT_decision_tree.png,The accuracy for the presented tree is higher than 78%.,12975
WineQT_decision_tree.png,The recall for the presented tree is higher than 66%.,12976
WineQT_decision_tree.png,The precision for the presented tree is higher than 81%.,12977
WineQT_decision_tree.png,The specificity for the presented tree is higher than 88%.,12978
WineQT_decision_tree.png,The accuracy for the presented tree is lower than 62%.,12979
WineQT_decision_tree.png,The recall for the presented tree is lower than 74%.,12980
WineQT_decision_tree.png,The precision for the presented tree is lower than 72%.,12981
WineQT_decision_tree.png,The specificity for the presented tree is lower than 69%.,12982
WineQT_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",12983
WineQT_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",12984
WineQT_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",12985
WineQT_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",12986
WineQT_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",12987
WineQT_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",12988
WineQT_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,12989
WineQT_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,12990
WineQT_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,12991
WineQT_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,12992
WineQT_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,12993
WineQT_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,12994
WineQT_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,12995
WineQT_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,12996
WineQT_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,12997
WineQT_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,12998
WineQT_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,12999
WineQT_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,13000
WineQT_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,13001
WineQT_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,13002
WineQT_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,13003
WineQT_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,13004
WineQT_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,13005
WineQT_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,13006
WineQT_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,13007
WineQT_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,13008
WineQT_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,13009
WineQT_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,13010
WineQT_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,13011
WineQT_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,13012
WineQT_overfitting_knn.png,KNN is in overfitting for k less than 2.,13013
WineQT_overfitting_knn.png,KNN is in overfitting for k larger than 2.,13014
WineQT_overfitting_knn.png,KNN is in overfitting for k less than 3.,13015
WineQT_overfitting_knn.png,KNN is in overfitting for k larger than 3.,13016
WineQT_overfitting_knn.png,KNN is in overfitting for k less than 4.,13017
WineQT_overfitting_knn.png,KNN is in overfitting for k larger than 4.,13018
WineQT_overfitting_knn.png,KNN is in overfitting for k less than 5.,13019
WineQT_overfitting_knn.png,KNN is in overfitting for k larger than 5.,13020
WineQT_overfitting_knn.png,KNN is in overfitting for k less than 6.,13021
WineQT_overfitting_knn.png,KNN is in overfitting for k larger than 6.,13022
WineQT_overfitting_knn.png,KNN is in overfitting for k less than 7.,13023
WineQT_overfitting_knn.png,KNN is in overfitting for k larger than 7.,13024
WineQT_overfitting_knn.png,KNN is in overfitting for k less than 8.,13025
WineQT_overfitting_knn.png,KNN is in overfitting for k larger than 8.,13026
WineQT_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",13027
WineQT_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",13028
WineQT_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",13029
WineQT_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",13030
WineQT_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",13031
WineQT_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",13032
WineQT_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",13033
WineQT_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",13034
WineQT_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",13035
WineQT_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",13036
WineQT_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",13037
WineQT_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",13038
WineQT_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",13039
WineQT_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",13040
WineQT_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,13041
WineQT_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,13042
WineQT_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,13043
WineQT_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,13044
WineQT_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,13045
WineQT_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,13046
WineQT_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,13047
WineQT_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,13048
WineQT_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,13049
WineQT_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,13050
WineQT_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,13051
WineQT_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,13052
WineQT_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,13053
WineQT_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,13054
WineQT_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,13055
WineQT_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,13056
WineQT_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,13057
WineQT_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,13058
WineQT_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,13059
WineQT_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,13060
WineQT_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,13061
WineQT_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,13062
WineQT_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,13063
WineQT_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,13064
WineQT_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,13065
WineQT_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,13066
WineQT_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,13067
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable fixed acidity.,13068
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable fixed acidity.,13069
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable fixed acidity.,13070
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable fixed acidity.,13071
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable fixed acidity.,13072
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable fixed acidity.,13073
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable fixed acidity.,13074
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable fixed acidity.,13075
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable fixed acidity.,13076
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable fixed acidity.,13077
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable fixed acidity.,13078
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable volatile acidity.,13079
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable volatile acidity.,13080
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable volatile acidity.,13081
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable volatile acidity.,13082
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable volatile acidity.,13083
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable volatile acidity.,13084
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable volatile acidity.,13085
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable volatile acidity.,13086
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable volatile acidity.,13087
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable volatile acidity.,13088
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable volatile acidity.,13089
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable citric acid.,13090
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable citric acid.,13091
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable citric acid.,13092
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable citric acid.,13093
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable citric acid.,13094
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable citric acid.,13095
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable citric acid.,13096
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable citric acid.,13097
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable citric acid.,13098
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable citric acid.,13099
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable citric acid.,13100
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable residual sugar.,13101
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable residual sugar.,13102
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable residual sugar.,13103
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable residual sugar.,13104
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable residual sugar.,13105
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable residual sugar.,13106
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable residual sugar.,13107
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable residual sugar.,13108
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable residual sugar.,13109
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable residual sugar.,13110
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable residual sugar.,13111
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable chlorides.,13112
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable chlorides.,13113
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable chlorides.,13114
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable chlorides.,13115
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable chlorides.,13116
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable chlorides.,13117
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable chlorides.,13118
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable chlorides.,13119
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable chlorides.,13120
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable chlorides.,13121
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable chlorides.,13122
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable free sulfur dioxide.,13123
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable free sulfur dioxide.,13124
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable free sulfur dioxide.,13125
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable free sulfur dioxide.,13126
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable free sulfur dioxide.,13127
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable free sulfur dioxide.,13128
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable free sulfur dioxide.,13129
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable free sulfur dioxide.,13130
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable free sulfur dioxide.,13131
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable free sulfur dioxide.,13132
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable free sulfur dioxide.,13133
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable total sulfur dioxide.,13134
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable total sulfur dioxide.,13135
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable total sulfur dioxide.,13136
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable total sulfur dioxide.,13137
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable total sulfur dioxide.,13138
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable total sulfur dioxide.,13139
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable total sulfur dioxide.,13140
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable total sulfur dioxide.,13141
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable total sulfur dioxide.,13142
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable total sulfur dioxide.,13143
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable total sulfur dioxide.,13144
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable density.,13145
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable density.,13146
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable density.,13147
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable density.,13148
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable density.,13149
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable density.,13150
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable density.,13151
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable density.,13152
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable density.,13153
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable density.,13154
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable density.,13155
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable pH.,13156
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable pH.,13157
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable pH.,13158
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable pH.,13159
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable pH.,13160
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable pH.,13161
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable pH.,13162
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable pH.,13163
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable pH.,13164
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable pH.,13165
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable pH.,13166
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable sulphates.,13167
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable sulphates.,13168
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable sulphates.,13169
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable sulphates.,13170
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable sulphates.,13171
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable sulphates.,13172
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable sulphates.,13173
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable sulphates.,13174
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable sulphates.,13175
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable sulphates.,13176
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable sulphates.,13177
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable alcohol.,13178
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable alcohol.,13179
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable alcohol.,13180
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable alcohol.,13181
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable alcohol.,13182
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable alcohol.,13183
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable alcohol.,13184
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable alcohol.,13185
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable alcohol.,13186
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable alcohol.,13187
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable quality previously than variable alcohol.,13188
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fixed acidity previously than variable quality.,13189
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable volatile acidity previously than variable quality.,13190
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable citric acid previously than variable quality.,13191
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable residual sugar previously than variable quality.,13192
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chlorides previously than variable quality.,13193
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable free sulfur dioxide previously than variable quality.,13194
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable total sulfur dioxide previously than variable quality.,13195
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable density previously than variable quality.,13196
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable pH previously than variable quality.,13197
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable sulphates previously than variable quality.,13198
WineQT_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable alcohol previously than variable quality.,13199
WineQT_pca.png,The first 2 principal components are enough for explaining half the data variance.,13200
WineQT_pca.png,The first 3 principal components are enough for explaining half the data variance.,13201
WineQT_pca.png,The first 4 principal components are enough for explaining half the data variance.,13202
WineQT_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,13203
WineQT_correlation_heatmap.png,Removing variable fixed acidity might improve the training of decision trees .,13204
WineQT_correlation_heatmap.png,Removing variable volatile acidity might improve the training of decision trees .,13205
WineQT_correlation_heatmap.png,Removing variable citric acid might improve the training of decision trees .,13206
WineQT_correlation_heatmap.png,Removing variable residual sugar might improve the training of decision trees .,13207
WineQT_correlation_heatmap.png,Removing variable chlorides might improve the training of decision trees .,13208
WineQT_correlation_heatmap.png,Removing variable free sulfur dioxide might improve the training of decision trees .,13209
WineQT_correlation_heatmap.png,Removing variable total sulfur dioxide might improve the training of decision trees .,13210
WineQT_correlation_heatmap.png,Removing variable density might improve the training of decision trees .,13211
WineQT_correlation_heatmap.png,Removing variable pH might improve the training of decision trees .,13212
WineQT_correlation_heatmap.png,Removing variable sulphates might improve the training of decision trees .,13213
WineQT_correlation_heatmap.png,Removing variable alcohol might improve the training of decision trees .,13214
WineQT_correlation_heatmap.png,Removing variable quality might improve the training of decision trees .,13215
WineQT_histograms.png,"Not knowing the semantics of fixed acidity variable, dummification could have been a more adequate codification.",13216
WineQT_histograms.png,"Not knowing the semantics of volatile acidity variable, dummification could have been a more adequate codification.",13217
WineQT_histograms.png,"Not knowing the semantics of citric acid variable, dummification could have been a more adequate codification.",13218
WineQT_histograms.png,"Not knowing the semantics of residual sugar variable, dummification could have been a more adequate codification.",13219
WineQT_histograms.png,"Not knowing the semantics of chlorides variable, dummification could have been a more adequate codification.",13220
WineQT_histograms.png,"Not knowing the semantics of free sulfur dioxide variable, dummification could have been a more adequate codification.",13221
WineQT_histograms.png,"Not knowing the semantics of total sulfur dioxide variable, dummification could have been a more adequate codification.",13222
WineQT_histograms.png,"Not knowing the semantics of density variable, dummification could have been a more adequate codification.",13223
WineQT_histograms.png,"Not knowing the semantics of pH variable, dummification could have been a more adequate codification.",13224
WineQT_histograms.png,"Not knowing the semantics of sulphates variable, dummification could have been a more adequate codification.",13225
WineQT_histograms.png,"Not knowing the semantics of alcohol variable, dummification could have been a more adequate codification.",13226
WineQT_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,13227
WineQT_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",13228
WineQT_histograms.png,"Given the usual semantics of fixed acidity variable, dummification would have been a better codification.",13229
WineQT_histograms.png,"Given the usual semantics of volatile acidity variable, dummification would have been a better codification.",13230
WineQT_histograms.png,"Given the usual semantics of citric acid variable, dummification would have been a better codification.",13231
WineQT_histograms.png,"Given the usual semantics of residual sugar variable, dummification would have been a better codification.",13232
WineQT_histograms.png,"Given the usual semantics of chlorides variable, dummification would have been a better codification.",13233
WineQT_histograms.png,"Given the usual semantics of free sulfur dioxide variable, dummification would have been a better codification.",13234
WineQT_histograms.png,"Given the usual semantics of total sulfur dioxide variable, dummification would have been a better codification.",13235
WineQT_histograms.png,"Given the usual semantics of density variable, dummification would have been a better codification.",13236
WineQT_histograms.png,"Given the usual semantics of pH variable, dummification would have been a better codification.",13237
WineQT_histograms.png,"Given the usual semantics of sulphates variable, dummification would have been a better codification.",13238
WineQT_histograms.png,"Given the usual semantics of alcohol variable, dummification would have been a better codification.",13239
WineQT_histograms.png,The variable fixed acidity can be coded as ordinal without losing information.,13240
WineQT_histograms.png,The variable volatile acidity can be coded as ordinal without losing information.,13241
WineQT_histograms.png,The variable citric acid can be coded as ordinal without losing information.,13242
WineQT_histograms.png,The variable residual sugar can be coded as ordinal without losing information.,13243
WineQT_histograms.png,The variable chlorides can be coded as ordinal without losing information.,13244
WineQT_histograms.png,The variable free sulfur dioxide can be coded as ordinal without losing information.,13245
WineQT_histograms.png,The variable total sulfur dioxide can be coded as ordinal without losing information.,13246
WineQT_histograms.png,The variable density can be coded as ordinal without losing information.,13247
WineQT_histograms.png,The variable pH can be coded as ordinal without losing information.,13248
WineQT_histograms.png,The variable sulphates can be coded as ordinal without losing information.,13249
WineQT_histograms.png,The variable alcohol can be coded as ordinal without losing information.,13250
WineQT_histograms.png,"Considering the common semantics for fixed acidity variable, dummification would be the most adequate encoding.",13251
WineQT_histograms.png,"Considering the common semantics for volatile acidity variable, dummification would be the most adequate encoding.",13252
WineQT_histograms.png,"Considering the common semantics for citric acid variable, dummification would be the most adequate encoding.",13253
WineQT_histograms.png,"Considering the common semantics for residual sugar variable, dummification would be the most adequate encoding.",13254
WineQT_histograms.png,"Considering the common semantics for chlorides variable, dummification would be the most adequate encoding.",13255
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide variable, dummification would be the most adequate encoding.",13256
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide variable, dummification would be the most adequate encoding.",13257
WineQT_histograms.png,"Considering the common semantics for density variable, dummification would be the most adequate encoding.",13258
WineQT_histograms.png,"Considering the common semantics for pH variable, dummification would be the most adequate encoding.",13259
WineQT_histograms.png,"Considering the common semantics for sulphates variable, dummification would be the most adequate encoding.",13260
WineQT_histograms.png,"Considering the common semantics for alcohol variable, dummification would be the most adequate encoding.",13261
WineQT_histograms.png,"Considering the common semantics for volatile acidity and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13262
WineQT_histograms.png,"Considering the common semantics for citric acid and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13263
WineQT_histograms.png,"Considering the common semantics for residual sugar and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13264
WineQT_histograms.png,"Considering the common semantics for chlorides and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13265
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13266
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13267
WineQT_histograms.png,"Considering the common semantics for density and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13268
WineQT_histograms.png,"Considering the common semantics for pH and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13269
WineQT_histograms.png,"Considering the common semantics for sulphates and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13270
WineQT_histograms.png,"Considering the common semantics for alcohol and fixed acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13271
WineQT_histograms.png,"Considering the common semantics for fixed acidity and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13272
WineQT_histograms.png,"Considering the common semantics for citric acid and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13273
WineQT_histograms.png,"Considering the common semantics for residual sugar and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13274
WineQT_histograms.png,"Considering the common semantics for chlorides and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13275
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13276
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13277
WineQT_histograms.png,"Considering the common semantics for density and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13278
WineQT_histograms.png,"Considering the common semantics for pH and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13279
WineQT_histograms.png,"Considering the common semantics for sulphates and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13280
WineQT_histograms.png,"Considering the common semantics for alcohol and volatile acidity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13281
WineQT_histograms.png,"Considering the common semantics for fixed acidity and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13282
WineQT_histograms.png,"Considering the common semantics for volatile acidity and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13283
WineQT_histograms.png,"Considering the common semantics for residual sugar and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13284
WineQT_histograms.png,"Considering the common semantics for chlorides and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13285
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13286
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13287
WineQT_histograms.png,"Considering the common semantics for density and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13288
WineQT_histograms.png,"Considering the common semantics for pH and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13289
WineQT_histograms.png,"Considering the common semantics for sulphates and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13290
WineQT_histograms.png,"Considering the common semantics for alcohol and citric acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13291
WineQT_histograms.png,"Considering the common semantics for fixed acidity and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13292
WineQT_histograms.png,"Considering the common semantics for volatile acidity and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13293
WineQT_histograms.png,"Considering the common semantics for citric acid and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13294
WineQT_histograms.png,"Considering the common semantics for chlorides and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13295
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13296
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13297
WineQT_histograms.png,"Considering the common semantics for density and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13298
WineQT_histograms.png,"Considering the common semantics for pH and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13299
WineQT_histograms.png,"Considering the common semantics for sulphates and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13300
WineQT_histograms.png,"Considering the common semantics for alcohol and residual sugar variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13301
WineQT_histograms.png,"Considering the common semantics for fixed acidity and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13302
WineQT_histograms.png,"Considering the common semantics for volatile acidity and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13303
WineQT_histograms.png,"Considering the common semantics for citric acid and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13304
WineQT_histograms.png,"Considering the common semantics for residual sugar and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13305
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13306
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13307
WineQT_histograms.png,"Considering the common semantics for density and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13308
WineQT_histograms.png,"Considering the common semantics for pH and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13309
WineQT_histograms.png,"Considering the common semantics for sulphates and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13310
WineQT_histograms.png,"Considering the common semantics for alcohol and chlorides variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13311
WineQT_histograms.png,"Considering the common semantics for fixed acidity and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13312
WineQT_histograms.png,"Considering the common semantics for volatile acidity and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13313
WineQT_histograms.png,"Considering the common semantics for citric acid and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13314
WineQT_histograms.png,"Considering the common semantics for residual sugar and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13315
WineQT_histograms.png,"Considering the common semantics for chlorides and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13316
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13317
WineQT_histograms.png,"Considering the common semantics for density and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13318
WineQT_histograms.png,"Considering the common semantics for pH and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13319
WineQT_histograms.png,"Considering the common semantics for sulphates and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13320
WineQT_histograms.png,"Considering the common semantics for alcohol and free sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13321
WineQT_histograms.png,"Considering the common semantics for fixed acidity and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13322
WineQT_histograms.png,"Considering the common semantics for volatile acidity and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13323
WineQT_histograms.png,"Considering the common semantics for citric acid and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13324
WineQT_histograms.png,"Considering the common semantics for residual sugar and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13325
WineQT_histograms.png,"Considering the common semantics for chlorides and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13326
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13327
WineQT_histograms.png,"Considering the common semantics for density and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13328
WineQT_histograms.png,"Considering the common semantics for pH and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13329
WineQT_histograms.png,"Considering the common semantics for sulphates and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13330
WineQT_histograms.png,"Considering the common semantics for alcohol and total sulfur dioxide variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13331
WineQT_histograms.png,"Considering the common semantics for fixed acidity and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13332
WineQT_histograms.png,"Considering the common semantics for volatile acidity and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13333
WineQT_histograms.png,"Considering the common semantics for citric acid and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13334
WineQT_histograms.png,"Considering the common semantics for residual sugar and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13335
WineQT_histograms.png,"Considering the common semantics for chlorides and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13336
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13337
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13338
WineQT_histograms.png,"Considering the common semantics for pH and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13339
WineQT_histograms.png,"Considering the common semantics for sulphates and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13340
WineQT_histograms.png,"Considering the common semantics for alcohol and density variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13341
WineQT_histograms.png,"Considering the common semantics for fixed acidity and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13342
WineQT_histograms.png,"Considering the common semantics for volatile acidity and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13343
WineQT_histograms.png,"Considering the common semantics for citric acid and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13344
WineQT_histograms.png,"Considering the common semantics for residual sugar and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13345
WineQT_histograms.png,"Considering the common semantics for chlorides and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13346
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13347
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13348
WineQT_histograms.png,"Considering the common semantics for density and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13349
WineQT_histograms.png,"Considering the common semantics for sulphates and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13350
WineQT_histograms.png,"Considering the common semantics for alcohol and pH variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13351
WineQT_histograms.png,"Considering the common semantics for fixed acidity and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13352
WineQT_histograms.png,"Considering the common semantics for volatile acidity and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13353
WineQT_histograms.png,"Considering the common semantics for citric acid and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13354
WineQT_histograms.png,"Considering the common semantics for residual sugar and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13355
WineQT_histograms.png,"Considering the common semantics for chlorides and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13356
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13357
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13358
WineQT_histograms.png,"Considering the common semantics for density and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13359
WineQT_histograms.png,"Considering the common semantics for pH and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13360
WineQT_histograms.png,"Considering the common semantics for alcohol and sulphates variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13361
WineQT_histograms.png,"Considering the common semantics for fixed acidity and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13362
WineQT_histograms.png,"Considering the common semantics for volatile acidity and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13363
WineQT_histograms.png,"Considering the common semantics for citric acid and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13364
WineQT_histograms.png,"Considering the common semantics for residual sugar and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13365
WineQT_histograms.png,"Considering the common semantics for chlorides and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13366
WineQT_histograms.png,"Considering the common semantics for free sulfur dioxide and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13367
WineQT_histograms.png,"Considering the common semantics for total sulfur dioxide and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13368
WineQT_histograms.png,"Considering the common semantics for density and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13369
WineQT_histograms.png,"Considering the common semantics for pH and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13370
WineQT_histograms.png,"Considering the common semantics for sulphates and alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",13371
WineQT_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,13372
WineQT_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,13373
WineQT_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,13374
WineQT_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",13375
WineQT_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",13376
WineQT_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",13377
WineQT_correlation_heatmap.png,Variables volatile acidity and fixed acidity seem to be useful for classification tasks.,13378
WineQT_correlation_heatmap.png,Variables citric acid and fixed acidity seem to be useful for classification tasks.,13379
WineQT_correlation_heatmap.png,Variables residual sugar and fixed acidity seem to be useful for classification tasks.,13380
WineQT_correlation_heatmap.png,Variables chlorides and fixed acidity seem to be useful for classification tasks.,13381
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and fixed acidity seem to be useful for classification tasks.,13382
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and fixed acidity seem to be useful for classification tasks.,13383
WineQT_correlation_heatmap.png,Variables density and fixed acidity seem to be useful for classification tasks.,13384
WineQT_correlation_heatmap.png,Variables pH and fixed acidity seem to be useful for classification tasks.,13385
WineQT_correlation_heatmap.png,Variables sulphates and fixed acidity seem to be useful for classification tasks.,13386
WineQT_correlation_heatmap.png,Variables alcohol and fixed acidity seem to be useful for classification tasks.,13387
WineQT_correlation_heatmap.png,Variables quality and fixed acidity seem to be useful for classification tasks.,13388
WineQT_correlation_heatmap.png,Variables fixed acidity and volatile acidity seem to be useful for classification tasks.,13389
WineQT_correlation_heatmap.png,Variables citric acid and volatile acidity seem to be useful for classification tasks.,13390
WineQT_correlation_heatmap.png,Variables residual sugar and volatile acidity seem to be useful for classification tasks.,13391
WineQT_correlation_heatmap.png,Variables chlorides and volatile acidity seem to be useful for classification tasks.,13392
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and volatile acidity seem to be useful for classification tasks.,13393
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and volatile acidity seem to be useful for classification tasks.,13394
WineQT_correlation_heatmap.png,Variables density and volatile acidity seem to be useful for classification tasks.,13395
WineQT_correlation_heatmap.png,Variables pH and volatile acidity seem to be useful for classification tasks.,13396
WineQT_correlation_heatmap.png,Variables sulphates and volatile acidity seem to be useful for classification tasks.,13397
WineQT_correlation_heatmap.png,Variables alcohol and volatile acidity seem to be useful for classification tasks.,13398
WineQT_correlation_heatmap.png,Variables quality and volatile acidity seem to be useful for classification tasks.,13399
WineQT_correlation_heatmap.png,Variables fixed acidity and citric acid seem to be useful for classification tasks.,13400
WineQT_correlation_heatmap.png,Variables volatile acidity and citric acid seem to be useful for classification tasks.,13401
WineQT_correlation_heatmap.png,Variables residual sugar and citric acid seem to be useful for classification tasks.,13402
WineQT_correlation_heatmap.png,Variables chlorides and citric acid seem to be useful for classification tasks.,13403
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and citric acid seem to be useful for classification tasks.,13404
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and citric acid seem to be useful for classification tasks.,13405
WineQT_correlation_heatmap.png,Variables density and citric acid seem to be useful for classification tasks.,13406
WineQT_correlation_heatmap.png,Variables pH and citric acid seem to be useful for classification tasks.,13407
WineQT_correlation_heatmap.png,Variables sulphates and citric acid seem to be useful for classification tasks.,13408
WineQT_correlation_heatmap.png,Variables alcohol and citric acid seem to be useful for classification tasks.,13409
WineQT_correlation_heatmap.png,Variables quality and citric acid seem to be useful for classification tasks.,13410
WineQT_correlation_heatmap.png,Variables fixed acidity and residual sugar seem to be useful for classification tasks.,13411
WineQT_correlation_heatmap.png,Variables volatile acidity and residual sugar seem to be useful for classification tasks.,13412
WineQT_correlation_heatmap.png,Variables citric acid and residual sugar seem to be useful for classification tasks.,13413
WineQT_correlation_heatmap.png,Variables chlorides and residual sugar seem to be useful for classification tasks.,13414
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and residual sugar seem to be useful for classification tasks.,13415
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and residual sugar seem to be useful for classification tasks.,13416
WineQT_correlation_heatmap.png,Variables density and residual sugar seem to be useful for classification tasks.,13417
WineQT_correlation_heatmap.png,Variables pH and residual sugar seem to be useful for classification tasks.,13418
WineQT_correlation_heatmap.png,Variables sulphates and residual sugar seem to be useful for classification tasks.,13419
WineQT_correlation_heatmap.png,Variables alcohol and residual sugar seem to be useful for classification tasks.,13420
WineQT_correlation_heatmap.png,Variables quality and residual sugar seem to be useful for classification tasks.,13421
WineQT_correlation_heatmap.png,Variables fixed acidity and chlorides seem to be useful for classification tasks.,13422
WineQT_correlation_heatmap.png,Variables volatile acidity and chlorides seem to be useful for classification tasks.,13423
WineQT_correlation_heatmap.png,Variables citric acid and chlorides seem to be useful for classification tasks.,13424
WineQT_correlation_heatmap.png,Variables residual sugar and chlorides seem to be useful for classification tasks.,13425
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and chlorides seem to be useful for classification tasks.,13426
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and chlorides seem to be useful for classification tasks.,13427
WineQT_correlation_heatmap.png,Variables density and chlorides seem to be useful for classification tasks.,13428
WineQT_correlation_heatmap.png,Variables pH and chlorides seem to be useful for classification tasks.,13429
WineQT_correlation_heatmap.png,Variables sulphates and chlorides seem to be useful for classification tasks.,13430
WineQT_correlation_heatmap.png,Variables alcohol and chlorides seem to be useful for classification tasks.,13431
WineQT_correlation_heatmap.png,Variables quality and chlorides seem to be useful for classification tasks.,13432
WineQT_correlation_heatmap.png,Variables fixed acidity and free sulfur dioxide seem to be useful for classification tasks.,13433
WineQT_correlation_heatmap.png,Variables volatile acidity and free sulfur dioxide seem to be useful for classification tasks.,13434
WineQT_correlation_heatmap.png,Variables citric acid and free sulfur dioxide seem to be useful for classification tasks.,13435
WineQT_correlation_heatmap.png,Variables residual sugar and free sulfur dioxide seem to be useful for classification tasks.,13436
WineQT_correlation_heatmap.png,Variables chlorides and free sulfur dioxide seem to be useful for classification tasks.,13437
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and free sulfur dioxide seem to be useful for classification tasks.,13438
WineQT_correlation_heatmap.png,Variables density and free sulfur dioxide seem to be useful for classification tasks.,13439
WineQT_correlation_heatmap.png,Variables pH and free sulfur dioxide seem to be useful for classification tasks.,13440
WineQT_correlation_heatmap.png,Variables sulphates and free sulfur dioxide seem to be useful for classification tasks.,13441
WineQT_correlation_heatmap.png,Variables alcohol and free sulfur dioxide seem to be useful for classification tasks.,13442
WineQT_correlation_heatmap.png,Variables quality and free sulfur dioxide seem to be useful for classification tasks.,13443
WineQT_correlation_heatmap.png,Variables fixed acidity and total sulfur dioxide seem to be useful for classification tasks.,13444
WineQT_correlation_heatmap.png,Variables volatile acidity and total sulfur dioxide seem to be useful for classification tasks.,13445
WineQT_correlation_heatmap.png,Variables citric acid and total sulfur dioxide seem to be useful for classification tasks.,13446
WineQT_correlation_heatmap.png,Variables residual sugar and total sulfur dioxide seem to be useful for classification tasks.,13447
WineQT_correlation_heatmap.png,Variables chlorides and total sulfur dioxide seem to be useful for classification tasks.,13448
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and total sulfur dioxide seem to be useful for classification tasks.,13449
WineQT_correlation_heatmap.png,Variables density and total sulfur dioxide seem to be useful for classification tasks.,13450
WineQT_correlation_heatmap.png,Variables pH and total sulfur dioxide seem to be useful for classification tasks.,13451
WineQT_correlation_heatmap.png,Variables sulphates and total sulfur dioxide seem to be useful for classification tasks.,13452
WineQT_correlation_heatmap.png,Variables alcohol and total sulfur dioxide seem to be useful for classification tasks.,13453
WineQT_correlation_heatmap.png,Variables quality and total sulfur dioxide seem to be useful for classification tasks.,13454
WineQT_correlation_heatmap.png,Variables fixed acidity and density seem to be useful for classification tasks.,13455
WineQT_correlation_heatmap.png,Variables volatile acidity and density seem to be useful for classification tasks.,13456
WineQT_correlation_heatmap.png,Variables citric acid and density seem to be useful for classification tasks.,13457
WineQT_correlation_heatmap.png,Variables residual sugar and density seem to be useful for classification tasks.,13458
WineQT_correlation_heatmap.png,Variables chlorides and density seem to be useful for classification tasks.,13459
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and density seem to be useful for classification tasks.,13460
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and density seem to be useful for classification tasks.,13461
WineQT_correlation_heatmap.png,Variables pH and density seem to be useful for classification tasks.,13462
WineQT_correlation_heatmap.png,Variables sulphates and density seem to be useful for classification tasks.,13463
WineQT_correlation_heatmap.png,Variables alcohol and density seem to be useful for classification tasks.,13464
WineQT_correlation_heatmap.png,Variables quality and density seem to be useful for classification tasks.,13465
WineQT_correlation_heatmap.png,Variables fixed acidity and pH seem to be useful for classification tasks.,13466
WineQT_correlation_heatmap.png,Variables volatile acidity and pH seem to be useful for classification tasks.,13467
WineQT_correlation_heatmap.png,Variables citric acid and pH seem to be useful for classification tasks.,13468
WineQT_correlation_heatmap.png,Variables residual sugar and pH seem to be useful for classification tasks.,13469
WineQT_correlation_heatmap.png,Variables chlorides and pH seem to be useful for classification tasks.,13470
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and pH seem to be useful for classification tasks.,13471
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and pH seem to be useful for classification tasks.,13472
WineQT_correlation_heatmap.png,Variables density and pH seem to be useful for classification tasks.,13473
WineQT_correlation_heatmap.png,Variables sulphates and pH seem to be useful for classification tasks.,13474
WineQT_correlation_heatmap.png,Variables alcohol and pH seem to be useful for classification tasks.,13475
WineQT_correlation_heatmap.png,Variables quality and pH seem to be useful for classification tasks.,13476
WineQT_correlation_heatmap.png,Variables fixed acidity and sulphates seem to be useful for classification tasks.,13477
WineQT_correlation_heatmap.png,Variables volatile acidity and sulphates seem to be useful for classification tasks.,13478
WineQT_correlation_heatmap.png,Variables citric acid and sulphates seem to be useful for classification tasks.,13479
WineQT_correlation_heatmap.png,Variables residual sugar and sulphates seem to be useful for classification tasks.,13480
WineQT_correlation_heatmap.png,Variables chlorides and sulphates seem to be useful for classification tasks.,13481
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and sulphates seem to be useful for classification tasks.,13482
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and sulphates seem to be useful for classification tasks.,13483
WineQT_correlation_heatmap.png,Variables density and sulphates seem to be useful for classification tasks.,13484
WineQT_correlation_heatmap.png,Variables pH and sulphates seem to be useful for classification tasks.,13485
WineQT_correlation_heatmap.png,Variables alcohol and sulphates seem to be useful for classification tasks.,13486
WineQT_correlation_heatmap.png,Variables quality and sulphates seem to be useful for classification tasks.,13487
WineQT_correlation_heatmap.png,Variables fixed acidity and alcohol seem to be useful for classification tasks.,13488
WineQT_correlation_heatmap.png,Variables volatile acidity and alcohol seem to be useful for classification tasks.,13489
WineQT_correlation_heatmap.png,Variables citric acid and alcohol seem to be useful for classification tasks.,13490
WineQT_correlation_heatmap.png,Variables residual sugar and alcohol seem to be useful for classification tasks.,13491
WineQT_correlation_heatmap.png,Variables chlorides and alcohol seem to be useful for classification tasks.,13492
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and alcohol seem to be useful for classification tasks.,13493
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and alcohol seem to be useful for classification tasks.,13494
WineQT_correlation_heatmap.png,Variables density and alcohol seem to be useful for classification tasks.,13495
WineQT_correlation_heatmap.png,Variables pH and alcohol seem to be useful for classification tasks.,13496
WineQT_correlation_heatmap.png,Variables sulphates and alcohol seem to be useful for classification tasks.,13497
WineQT_correlation_heatmap.png,Variables quality and alcohol seem to be useful for classification tasks.,13498
WineQT_correlation_heatmap.png,Variables fixed acidity and quality seem to be useful for classification tasks.,13499
WineQT_correlation_heatmap.png,Variables volatile acidity and quality seem to be useful for classification tasks.,13500
WineQT_correlation_heatmap.png,Variables citric acid and quality seem to be useful for classification tasks.,13501
WineQT_correlation_heatmap.png,Variables residual sugar and quality seem to be useful for classification tasks.,13502
WineQT_correlation_heatmap.png,Variables chlorides and quality seem to be useful for classification tasks.,13503
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and quality seem to be useful for classification tasks.,13504
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and quality seem to be useful for classification tasks.,13505
WineQT_correlation_heatmap.png,Variables density and quality seem to be useful for classification tasks.,13506
WineQT_correlation_heatmap.png,Variables pH and quality seem to be useful for classification tasks.,13507
WineQT_correlation_heatmap.png,Variables sulphates and quality seem to be useful for classification tasks.,13508
WineQT_correlation_heatmap.png,Variables alcohol and quality seem to be useful for classification tasks.,13509
WineQT_correlation_heatmap.png,Variable fixed acidity seems to be relevant for the majority of mining tasks.,13510
WineQT_correlation_heatmap.png,Variable volatile acidity seems to be relevant for the majority of mining tasks.,13511
WineQT_correlation_heatmap.png,Variable citric acid seems to be relevant for the majority of mining tasks.,13512
WineQT_correlation_heatmap.png,Variable residual sugar seems to be relevant for the majority of mining tasks.,13513
WineQT_correlation_heatmap.png,Variable chlorides seems to be relevant for the majority of mining tasks.,13514
WineQT_correlation_heatmap.png,Variable free sulfur dioxide seems to be relevant for the majority of mining tasks.,13515
WineQT_correlation_heatmap.png,Variable total sulfur dioxide seems to be relevant for the majority of mining tasks.,13516
WineQT_correlation_heatmap.png,Variable density seems to be relevant for the majority of mining tasks.,13517
WineQT_correlation_heatmap.png,Variable pH seems to be relevant for the majority of mining tasks.,13518
WineQT_correlation_heatmap.png,Variable sulphates seems to be relevant for the majority of mining tasks.,13519
WineQT_correlation_heatmap.png,Variable alcohol seems to be relevant for the majority of mining tasks.,13520
WineQT_correlation_heatmap.png,Variable quality seems to be relevant for the majority of mining tasks.,13521
WineQT_decision_tree.png,Variable fixed acidity is one of the most relevant variables.,13522
WineQT_decision_tree.png,Variable volatile acidity is one of the most relevant variables.,13523
WineQT_decision_tree.png,Variable citric acid is one of the most relevant variables.,13524
WineQT_decision_tree.png,Variable residual sugar is one of the most relevant variables.,13525
WineQT_decision_tree.png,Variable chlorides is one of the most relevant variables.,13526
WineQT_decision_tree.png,Variable free sulfur dioxide is one of the most relevant variables.,13527
WineQT_decision_tree.png,Variable total sulfur dioxide is one of the most relevant variables.,13528
WineQT_decision_tree.png,Variable density is one of the most relevant variables.,13529
WineQT_decision_tree.png,Variable pH is one of the most relevant variables.,13530
WineQT_decision_tree.png,Variable sulphates is one of the most relevant variables.,13531
WineQT_decision_tree.png,Variable alcohol is one of the most relevant variables.,13532
WineQT_decision_tree.png,Variable quality is one of the most relevant variables.,13533
WineQT_decision_tree.png,It is possible to state that fixed acidity is the first most discriminative variable regarding the class.,13534
WineQT_decision_tree.png,It is possible to state that volatile acidity is the first most discriminative variable regarding the class.,13535
WineQT_decision_tree.png,It is possible to state that citric acid is the first most discriminative variable regarding the class.,13536
WineQT_decision_tree.png,It is possible to state that residual sugar is the first most discriminative variable regarding the class.,13537
WineQT_decision_tree.png,It is possible to state that chlorides is the first most discriminative variable regarding the class.,13538
WineQT_decision_tree.png,It is possible to state that free sulfur dioxide is the first most discriminative variable regarding the class.,13539
WineQT_decision_tree.png,It is possible to state that total sulfur dioxide is the first most discriminative variable regarding the class.,13540
WineQT_decision_tree.png,It is possible to state that density is the first most discriminative variable regarding the class.,13541
WineQT_decision_tree.png,It is possible to state that pH is the first most discriminative variable regarding the class.,13542
WineQT_decision_tree.png,It is possible to state that sulphates is the first most discriminative variable regarding the class.,13543
WineQT_decision_tree.png,It is possible to state that alcohol is the first most discriminative variable regarding the class.,13544
WineQT_decision_tree.png,It is possible to state that quality is the first most discriminative variable regarding the class.,13545
WineQT_decision_tree.png,It is possible to state that fixed acidity is the second most discriminative variable regarding the class.,13546
WineQT_decision_tree.png,It is possible to state that volatile acidity is the second most discriminative variable regarding the class.,13547
WineQT_decision_tree.png,It is possible to state that citric acid is the second most discriminative variable regarding the class.,13548
WineQT_decision_tree.png,It is possible to state that residual sugar is the second most discriminative variable regarding the class.,13549
WineQT_decision_tree.png,It is possible to state that chlorides is the second most discriminative variable regarding the class.,13550
WineQT_decision_tree.png,It is possible to state that free sulfur dioxide is the second most discriminative variable regarding the class.,13551
WineQT_decision_tree.png,It is possible to state that total sulfur dioxide is the second most discriminative variable regarding the class.,13552
WineQT_decision_tree.png,It is possible to state that density is the second most discriminative variable regarding the class.,13553
WineQT_decision_tree.png,It is possible to state that pH is the second most discriminative variable regarding the class.,13554
WineQT_decision_tree.png,It is possible to state that sulphates is the second most discriminative variable regarding the class.,13555
WineQT_decision_tree.png,It is possible to state that alcohol is the second most discriminative variable regarding the class.,13556
WineQT_decision_tree.png,It is possible to state that quality is the second most discriminative variable regarding the class.,13557
WineQT_decision_tree.png,"The variable fixed acidity discriminates between the target values, as shown in the decision tree.",13558
WineQT_decision_tree.png,"The variable volatile acidity discriminates between the target values, as shown in the decision tree.",13559
WineQT_decision_tree.png,"The variable citric acid discriminates between the target values, as shown in the decision tree.",13560
WineQT_decision_tree.png,"The variable residual sugar discriminates between the target values, as shown in the decision tree.",13561
WineQT_decision_tree.png,"The variable chlorides discriminates between the target values, as shown in the decision tree.",13562
WineQT_decision_tree.png,"The variable free sulfur dioxide discriminates between the target values, as shown in the decision tree.",13563
WineQT_decision_tree.png,"The variable total sulfur dioxide discriminates between the target values, as shown in the decision tree.",13564
WineQT_decision_tree.png,"The variable density discriminates between the target values, as shown in the decision tree.",13565
WineQT_decision_tree.png,"The variable pH discriminates between the target values, as shown in the decision tree.",13566
WineQT_decision_tree.png,"The variable sulphates discriminates between the target values, as shown in the decision tree.",13567
WineQT_decision_tree.png,"The variable alcohol discriminates between the target values, as shown in the decision tree.",13568
WineQT_decision_tree.png,"The variable quality discriminates between the target values, as shown in the decision tree.",13569
WineQT_decision_tree.png,The variable fixed acidity seems to be one of the two most relevant features.,13570
WineQT_decision_tree.png,The variable volatile acidity seems to be one of the two most relevant features.,13571
WineQT_decision_tree.png,The variable citric acid seems to be one of the two most relevant features.,13572
WineQT_decision_tree.png,The variable residual sugar seems to be one of the two most relevant features.,13573
WineQT_decision_tree.png,The variable chlorides seems to be one of the two most relevant features.,13574
WineQT_decision_tree.png,The variable free sulfur dioxide seems to be one of the two most relevant features.,13575
WineQT_decision_tree.png,The variable total sulfur dioxide seems to be one of the two most relevant features.,13576
WineQT_decision_tree.png,The variable density seems to be one of the two most relevant features.,13577
WineQT_decision_tree.png,The variable pH seems to be one of the two most relevant features.,13578
WineQT_decision_tree.png,The variable sulphates seems to be one of the two most relevant features.,13579
WineQT_decision_tree.png,The variable alcohol seems to be one of the two most relevant features.,13580
WineQT_decision_tree.png,The variable quality seems to be one of the two most relevant features.,13581
WineQT_decision_tree.png,The variable fixed acidity seems to be one of the three most relevant features.,13582
WineQT_decision_tree.png,The variable volatile acidity seems to be one of the three most relevant features.,13583
WineQT_decision_tree.png,The variable citric acid seems to be one of the three most relevant features.,13584
WineQT_decision_tree.png,The variable residual sugar seems to be one of the three most relevant features.,13585
WineQT_decision_tree.png,The variable chlorides seems to be one of the three most relevant features.,13586
WineQT_decision_tree.png,The variable free sulfur dioxide seems to be one of the three most relevant features.,13587
WineQT_decision_tree.png,The variable total sulfur dioxide seems to be one of the three most relevant features.,13588
WineQT_decision_tree.png,The variable density seems to be one of the three most relevant features.,13589
WineQT_decision_tree.png,The variable pH seems to be one of the three most relevant features.,13590
WineQT_decision_tree.png,The variable sulphates seems to be one of the three most relevant features.,13591
WineQT_decision_tree.png,The variable alcohol seems to be one of the three most relevant features.,13592
WineQT_decision_tree.png,The variable quality seems to be one of the three most relevant features.,13593
WineQT_decision_tree.png,The variable fixed acidity seems to be one of the four most relevant features.,13594
WineQT_decision_tree.png,The variable volatile acidity seems to be one of the four most relevant features.,13595
WineQT_decision_tree.png,The variable citric acid seems to be one of the four most relevant features.,13596
WineQT_decision_tree.png,The variable residual sugar seems to be one of the four most relevant features.,13597
WineQT_decision_tree.png,The variable chlorides seems to be one of the four most relevant features.,13598
WineQT_decision_tree.png,The variable free sulfur dioxide seems to be one of the four most relevant features.,13599
WineQT_decision_tree.png,The variable total sulfur dioxide seems to be one of the four most relevant features.,13600
WineQT_decision_tree.png,The variable density seems to be one of the four most relevant features.,13601
WineQT_decision_tree.png,The variable pH seems to be one of the four most relevant features.,13602
WineQT_decision_tree.png,The variable sulphates seems to be one of the four most relevant features.,13603
WineQT_decision_tree.png,The variable alcohol seems to be one of the four most relevant features.,13604
WineQT_decision_tree.png,The variable quality seems to be one of the four most relevant features.,13605
WineQT_decision_tree.png,The variable fixed acidity seems to be one of the five most relevant features.,13606
WineQT_decision_tree.png,The variable volatile acidity seems to be one of the five most relevant features.,13607
WineQT_decision_tree.png,The variable citric acid seems to be one of the five most relevant features.,13608
WineQT_decision_tree.png,The variable residual sugar seems to be one of the five most relevant features.,13609
WineQT_decision_tree.png,The variable chlorides seems to be one of the five most relevant features.,13610
WineQT_decision_tree.png,The variable free sulfur dioxide seems to be one of the five most relevant features.,13611
WineQT_decision_tree.png,The variable total sulfur dioxide seems to be one of the five most relevant features.,13612
WineQT_decision_tree.png,The variable density seems to be one of the five most relevant features.,13613
WineQT_decision_tree.png,The variable pH seems to be one of the five most relevant features.,13614
WineQT_decision_tree.png,The variable sulphates seems to be one of the five most relevant features.,13615
WineQT_decision_tree.png,The variable alcohol seems to be one of the five most relevant features.,13616
WineQT_decision_tree.png,The variable quality seems to be one of the five most relevant features.,13617
WineQT_decision_tree.png,It is clear that variable fixed acidity is one of the two most relevant features.,13618
WineQT_decision_tree.png,It is clear that variable volatile acidity is one of the two most relevant features.,13619
WineQT_decision_tree.png,It is clear that variable citric acid is one of the two most relevant features.,13620
WineQT_decision_tree.png,It is clear that variable residual sugar is one of the two most relevant features.,13621
WineQT_decision_tree.png,It is clear that variable chlorides is one of the two most relevant features.,13622
WineQT_decision_tree.png,It is clear that variable free sulfur dioxide is one of the two most relevant features.,13623
WineQT_decision_tree.png,It is clear that variable total sulfur dioxide is one of the two most relevant features.,13624
WineQT_decision_tree.png,It is clear that variable density is one of the two most relevant features.,13625
WineQT_decision_tree.png,It is clear that variable pH is one of the two most relevant features.,13626
WineQT_decision_tree.png,It is clear that variable sulphates is one of the two most relevant features.,13627
WineQT_decision_tree.png,It is clear that variable alcohol is one of the two most relevant features.,13628
WineQT_decision_tree.png,It is clear that variable quality is one of the two most relevant features.,13629
WineQT_decision_tree.png,It is clear that variable fixed acidity is one of the three most relevant features.,13630
WineQT_decision_tree.png,It is clear that variable volatile acidity is one of the three most relevant features.,13631
WineQT_decision_tree.png,It is clear that variable citric acid is one of the three most relevant features.,13632
WineQT_decision_tree.png,It is clear that variable residual sugar is one of the three most relevant features.,13633
WineQT_decision_tree.png,It is clear that variable chlorides is one of the three most relevant features.,13634
WineQT_decision_tree.png,It is clear that variable free sulfur dioxide is one of the three most relevant features.,13635
WineQT_decision_tree.png,It is clear that variable total sulfur dioxide is one of the three most relevant features.,13636
WineQT_decision_tree.png,It is clear that variable density is one of the three most relevant features.,13637
WineQT_decision_tree.png,It is clear that variable pH is one of the three most relevant features.,13638
WineQT_decision_tree.png,It is clear that variable sulphates is one of the three most relevant features.,13639
WineQT_decision_tree.png,It is clear that variable alcohol is one of the three most relevant features.,13640
WineQT_decision_tree.png,It is clear that variable quality is one of the three most relevant features.,13641
WineQT_decision_tree.png,It is clear that variable fixed acidity is one of the four most relevant features.,13642
WineQT_decision_tree.png,It is clear that variable volatile acidity is one of the four most relevant features.,13643
WineQT_decision_tree.png,It is clear that variable citric acid is one of the four most relevant features.,13644
WineQT_decision_tree.png,It is clear that variable residual sugar is one of the four most relevant features.,13645
WineQT_decision_tree.png,It is clear that variable chlorides is one of the four most relevant features.,13646
WineQT_decision_tree.png,It is clear that variable free sulfur dioxide is one of the four most relevant features.,13647
WineQT_decision_tree.png,It is clear that variable total sulfur dioxide is one of the four most relevant features.,13648
WineQT_decision_tree.png,It is clear that variable density is one of the four most relevant features.,13649
WineQT_decision_tree.png,It is clear that variable pH is one of the four most relevant features.,13650
WineQT_decision_tree.png,It is clear that variable sulphates is one of the four most relevant features.,13651
WineQT_decision_tree.png,It is clear that variable alcohol is one of the four most relevant features.,13652
WineQT_decision_tree.png,It is clear that variable quality is one of the four most relevant features.,13653
WineQT_decision_tree.png,It is clear that variable fixed acidity is one of the five most relevant features.,13654
WineQT_decision_tree.png,It is clear that variable volatile acidity is one of the five most relevant features.,13655
WineQT_decision_tree.png,It is clear that variable citric acid is one of the five most relevant features.,13656
WineQT_decision_tree.png,It is clear that variable residual sugar is one of the five most relevant features.,13657
WineQT_decision_tree.png,It is clear that variable chlorides is one of the five most relevant features.,13658
WineQT_decision_tree.png,It is clear that variable free sulfur dioxide is one of the five most relevant features.,13659
WineQT_decision_tree.png,It is clear that variable total sulfur dioxide is one of the five most relevant features.,13660
WineQT_decision_tree.png,It is clear that variable density is one of the five most relevant features.,13661
WineQT_decision_tree.png,It is clear that variable pH is one of the five most relevant features.,13662
WineQT_decision_tree.png,It is clear that variable sulphates is one of the five most relevant features.,13663
WineQT_decision_tree.png,It is clear that variable alcohol is one of the five most relevant features.,13664
WineQT_decision_tree.png,It is clear that variable quality is one of the five most relevant features.,13665
WineQT_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",13666
WineQT_correlation_heatmap.png,Variables volatile acidity and fixed acidity are redundant.,13667
WineQT_correlation_heatmap.png,Variables citric acid and fixed acidity are redundant.,13668
WineQT_correlation_heatmap.png,Variables residual sugar and fixed acidity are redundant.,13669
WineQT_correlation_heatmap.png,Variables chlorides and fixed acidity are redundant.,13670
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and fixed acidity are redundant.,13671
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and fixed acidity are redundant.,13672
WineQT_correlation_heatmap.png,Variables density and fixed acidity are redundant.,13673
WineQT_correlation_heatmap.png,Variables pH and fixed acidity are redundant.,13674
WineQT_correlation_heatmap.png,Variables sulphates and fixed acidity are redundant.,13675
WineQT_correlation_heatmap.png,Variables alcohol and fixed acidity are redundant.,13676
WineQT_correlation_heatmap.png,Variables quality and fixed acidity are redundant.,13677
WineQT_correlation_heatmap.png,Variables fixed acidity and volatile acidity are redundant.,13678
WineQT_correlation_heatmap.png,Variables citric acid and volatile acidity are redundant.,13679
WineQT_correlation_heatmap.png,Variables residual sugar and volatile acidity are redundant.,13680
WineQT_correlation_heatmap.png,Variables chlorides and volatile acidity are redundant.,13681
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and volatile acidity are redundant.,13682
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and volatile acidity are redundant.,13683
WineQT_correlation_heatmap.png,Variables density and volatile acidity are redundant.,13684
WineQT_correlation_heatmap.png,Variables pH and volatile acidity are redundant.,13685
WineQT_correlation_heatmap.png,Variables sulphates and volatile acidity are redundant.,13686
WineQT_correlation_heatmap.png,Variables alcohol and volatile acidity are redundant.,13687
WineQT_correlation_heatmap.png,Variables quality and volatile acidity are redundant.,13688
WineQT_correlation_heatmap.png,Variables fixed acidity and citric acid are redundant.,13689
WineQT_correlation_heatmap.png,Variables volatile acidity and citric acid are redundant.,13690
WineQT_correlation_heatmap.png,Variables residual sugar and citric acid are redundant.,13691
WineQT_correlation_heatmap.png,Variables chlorides and citric acid are redundant.,13692
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and citric acid are redundant.,13693
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and citric acid are redundant.,13694
WineQT_correlation_heatmap.png,Variables density and citric acid are redundant.,13695
WineQT_correlation_heatmap.png,Variables pH and citric acid are redundant.,13696
WineQT_correlation_heatmap.png,Variables sulphates and citric acid are redundant.,13697
WineQT_correlation_heatmap.png,Variables alcohol and citric acid are redundant.,13698
WineQT_correlation_heatmap.png,Variables quality and citric acid are redundant.,13699
WineQT_correlation_heatmap.png,Variables fixed acidity and residual sugar are redundant.,13700
WineQT_correlation_heatmap.png,Variables volatile acidity and residual sugar are redundant.,13701
WineQT_correlation_heatmap.png,Variables citric acid and residual sugar are redundant.,13702
WineQT_correlation_heatmap.png,Variables chlorides and residual sugar are redundant.,13703
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and residual sugar are redundant.,13704
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and residual sugar are redundant.,13705
WineQT_correlation_heatmap.png,Variables density and residual sugar are redundant.,13706
WineQT_correlation_heatmap.png,Variables pH and residual sugar are redundant.,13707
WineQT_correlation_heatmap.png,Variables sulphates and residual sugar are redundant.,13708
WineQT_correlation_heatmap.png,Variables alcohol and residual sugar are redundant.,13709
WineQT_correlation_heatmap.png,Variables quality and residual sugar are redundant.,13710
WineQT_correlation_heatmap.png,Variables fixed acidity and chlorides are redundant.,13711
WineQT_correlation_heatmap.png,Variables volatile acidity and chlorides are redundant.,13712
WineQT_correlation_heatmap.png,Variables citric acid and chlorides are redundant.,13713
WineQT_correlation_heatmap.png,Variables residual sugar and chlorides are redundant.,13714
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and chlorides are redundant.,13715
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and chlorides are redundant.,13716
WineQT_correlation_heatmap.png,Variables density and chlorides are redundant.,13717
WineQT_correlation_heatmap.png,Variables pH and chlorides are redundant.,13718
WineQT_correlation_heatmap.png,Variables sulphates and chlorides are redundant.,13719
WineQT_correlation_heatmap.png,Variables alcohol and chlorides are redundant.,13720
WineQT_correlation_heatmap.png,Variables quality and chlorides are redundant.,13721
WineQT_correlation_heatmap.png,Variables fixed acidity and free sulfur dioxide are redundant.,13722
WineQT_correlation_heatmap.png,Variables volatile acidity and free sulfur dioxide are redundant.,13723
WineQT_correlation_heatmap.png,Variables citric acid and free sulfur dioxide are redundant.,13724
WineQT_correlation_heatmap.png,Variables residual sugar and free sulfur dioxide are redundant.,13725
WineQT_correlation_heatmap.png,Variables chlorides and free sulfur dioxide are redundant.,13726
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and free sulfur dioxide are redundant.,13727
WineQT_correlation_heatmap.png,Variables density and free sulfur dioxide are redundant.,13728
WineQT_correlation_heatmap.png,Variables pH and free sulfur dioxide are redundant.,13729
WineQT_correlation_heatmap.png,Variables sulphates and free sulfur dioxide are redundant.,13730
WineQT_correlation_heatmap.png,Variables alcohol and free sulfur dioxide are redundant.,13731
WineQT_correlation_heatmap.png,Variables quality and free sulfur dioxide are redundant.,13732
WineQT_correlation_heatmap.png,Variables fixed acidity and total sulfur dioxide are redundant.,13733
WineQT_correlation_heatmap.png,Variables volatile acidity and total sulfur dioxide are redundant.,13734
WineQT_correlation_heatmap.png,Variables citric acid and total sulfur dioxide are redundant.,13735
WineQT_correlation_heatmap.png,Variables residual sugar and total sulfur dioxide are redundant.,13736
WineQT_correlation_heatmap.png,Variables chlorides and total sulfur dioxide are redundant.,13737
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and total sulfur dioxide are redundant.,13738
WineQT_correlation_heatmap.png,Variables density and total sulfur dioxide are redundant.,13739
WineQT_correlation_heatmap.png,Variables pH and total sulfur dioxide are redundant.,13740
WineQT_correlation_heatmap.png,Variables sulphates and total sulfur dioxide are redundant.,13741
WineQT_correlation_heatmap.png,Variables alcohol and total sulfur dioxide are redundant.,13742
WineQT_correlation_heatmap.png,Variables quality and total sulfur dioxide are redundant.,13743
WineQT_correlation_heatmap.png,Variables fixed acidity and density are redundant.,13744
WineQT_correlation_heatmap.png,Variables volatile acidity and density are redundant.,13745
WineQT_correlation_heatmap.png,Variables citric acid and density are redundant.,13746
WineQT_correlation_heatmap.png,Variables residual sugar and density are redundant.,13747
WineQT_correlation_heatmap.png,Variables chlorides and density are redundant.,13748
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and density are redundant.,13749
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and density are redundant.,13750
WineQT_correlation_heatmap.png,Variables pH and density are redundant.,13751
WineQT_correlation_heatmap.png,Variables sulphates and density are redundant.,13752
WineQT_correlation_heatmap.png,Variables alcohol and density are redundant.,13753
WineQT_correlation_heatmap.png,Variables quality and density are redundant.,13754
WineQT_correlation_heatmap.png,Variables fixed acidity and pH are redundant.,13755
WineQT_correlation_heatmap.png,Variables volatile acidity and pH are redundant.,13756
WineQT_correlation_heatmap.png,Variables citric acid and pH are redundant.,13757
WineQT_correlation_heatmap.png,Variables residual sugar and pH are redundant.,13758
WineQT_correlation_heatmap.png,Variables chlorides and pH are redundant.,13759
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and pH are redundant.,13760
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and pH are redundant.,13761
WineQT_correlation_heatmap.png,Variables density and pH are redundant.,13762
WineQT_correlation_heatmap.png,Variables sulphates and pH are redundant.,13763
WineQT_correlation_heatmap.png,Variables alcohol and pH are redundant.,13764
WineQT_correlation_heatmap.png,Variables quality and pH are redundant.,13765
WineQT_correlation_heatmap.png,Variables fixed acidity and sulphates are redundant.,13766
WineQT_correlation_heatmap.png,Variables volatile acidity and sulphates are redundant.,13767
WineQT_correlation_heatmap.png,Variables citric acid and sulphates are redundant.,13768
WineQT_correlation_heatmap.png,Variables residual sugar and sulphates are redundant.,13769
WineQT_correlation_heatmap.png,Variables chlorides and sulphates are redundant.,13770
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and sulphates are redundant.,13771
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and sulphates are redundant.,13772
WineQT_correlation_heatmap.png,Variables density and sulphates are redundant.,13773
WineQT_correlation_heatmap.png,Variables pH and sulphates are redundant.,13774
WineQT_correlation_heatmap.png,Variables alcohol and sulphates are redundant.,13775
WineQT_correlation_heatmap.png,Variables quality and sulphates are redundant.,13776
WineQT_correlation_heatmap.png,Variables fixed acidity and alcohol are redundant.,13777
WineQT_correlation_heatmap.png,Variables volatile acidity and alcohol are redundant.,13778
WineQT_correlation_heatmap.png,Variables citric acid and alcohol are redundant.,13779
WineQT_correlation_heatmap.png,Variables residual sugar and alcohol are redundant.,13780
WineQT_correlation_heatmap.png,Variables chlorides and alcohol are redundant.,13781
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and alcohol are redundant.,13782
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and alcohol are redundant.,13783
WineQT_correlation_heatmap.png,Variables density and alcohol are redundant.,13784
WineQT_correlation_heatmap.png,Variables pH and alcohol are redundant.,13785
WineQT_correlation_heatmap.png,Variables sulphates and alcohol are redundant.,13786
WineQT_correlation_heatmap.png,Variables quality and alcohol are redundant.,13787
WineQT_correlation_heatmap.png,Variables fixed acidity and quality are redundant.,13788
WineQT_correlation_heatmap.png,Variables volatile acidity and quality are redundant.,13789
WineQT_correlation_heatmap.png,Variables citric acid and quality are redundant.,13790
WineQT_correlation_heatmap.png,Variables residual sugar and quality are redundant.,13791
WineQT_correlation_heatmap.png,Variables chlorides and quality are redundant.,13792
WineQT_correlation_heatmap.png,Variables free sulfur dioxide and quality are redundant.,13793
WineQT_correlation_heatmap.png,Variables total sulfur dioxide and quality are redundant.,13794
WineQT_correlation_heatmap.png,Variables density and quality are redundant.,13795
WineQT_correlation_heatmap.png,Variables pH and quality are redundant.,13796
WineQT_correlation_heatmap.png,Variables sulphates and quality are redundant.,13797
WineQT_correlation_heatmap.png,Variables alcohol and quality are redundant.,13798
WineQT_correlation_heatmap.png,"Variables pH and alcohol are redundant, but we can’t say the same for the pair volatile acidity and free sulfur dioxide.",13799
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair residual sugar and alcohol.",13800
WineQT_correlation_heatmap.png,"Variables residual sugar and sulphates are redundant, but we can’t say the same for the pair citric acid and density.",13801
WineQT_correlation_heatmap.png,"Variables volatile acidity and sulphates are redundant, but we can’t say the same for the pair residual sugar and pH.",13802
WineQT_correlation_heatmap.png,"Variables fixed acidity and density are redundant, but we can’t say the same for the pair chlorides and sulphates.",13803
WineQT_correlation_heatmap.png,"Variables volatile acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and pH.",13804
WineQT_correlation_heatmap.png,"Variables fixed acidity and residual sugar are redundant, but we can’t say the same for the pair volatile acidity and sulphates.",13805
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and pH are redundant, but we can’t say the same for the pair chlorides and total sulfur dioxide.",13806
WineQT_correlation_heatmap.png,"Variables volatile acidity and alcohol are redundant, but we can’t say the same for the pair citric acid and sulphates.",13807
WineQT_correlation_heatmap.png,"Variables volatile acidity and sulphates are redundant, but we can’t say the same for the pair citric acid and total sulfur dioxide.",13808
WineQT_correlation_heatmap.png,"Variables volatile acidity and citric acid are redundant, but we can’t say the same for the pair fixed acidity and density.",13809
WineQT_correlation_heatmap.png,"Variables fixed acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair residual sugar and pH.",13810
WineQT_correlation_heatmap.png,"Variables citric acid and alcohol are redundant, but we can’t say the same for the pair volatile acidity and total sulfur dioxide.",13811
WineQT_correlation_heatmap.png,"Variables sulphates and alcohol are redundant, but we can’t say the same for the pair density and pH.",13812
WineQT_correlation_heatmap.png,"Variables volatile acidity and citric acid are redundant, but we can’t say the same for the pair residual sugar and total sulfur dioxide.",13813
WineQT_correlation_heatmap.png,"Variables residual sugar and chlorides are redundant, but we can’t say the same for the pair fixed acidity and pH.",13814
WineQT_correlation_heatmap.png,"Variables citric acid and pH are redundant, but we can’t say the same for the pair total sulfur dioxide and sulphates.",13815
WineQT_correlation_heatmap.png,"Variables volatile acidity and residual sugar are redundant, but we can’t say the same for the pair fixed acidity and density.",13816
WineQT_correlation_heatmap.png,"Variables volatile acidity and citric acid are redundant, but we can’t say the same for the pair fixed acidity and total sulfur dioxide.",13817
WineQT_correlation_heatmap.png,"Variables citric acid and alcohol are redundant, but we can’t say the same for the pair fixed acidity and free sulfur dioxide.",13818
WineQT_correlation_heatmap.png,"Variables fixed acidity and citric acid are redundant, but we can’t say the same for the pair residual sugar and total sulfur dioxide.",13819
WineQT_correlation_heatmap.png,"Variables density and sulphates are redundant, but we can’t say the same for the pair fixed acidity and alcohol.",13820
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and alcohol are redundant, but we can’t say the same for the pair residual sugar and pH.",13821
WineQT_correlation_heatmap.png,"Variables citric acid and free sulfur dioxide are redundant, but we can’t say the same for the pair volatile acidity and sulphates.",13822
WineQT_correlation_heatmap.png,"Variables volatile acidity and sulphates are redundant, but we can’t say the same for the pair residual sugar and alcohol.",13823
WineQT_correlation_heatmap.png,"Variables chlorides and density are redundant, but we can’t say the same for the pair citric acid and pH.",13824
WineQT_correlation_heatmap.png,"Variables fixed acidity and volatile acidity are redundant, but we can’t say the same for the pair citric acid and free sulfur dioxide.",13825
WineQT_correlation_heatmap.png,"Variables citric acid and residual sugar are redundant, but we can’t say the same for the pair fixed acidity and alcohol.",13826
WineQT_correlation_heatmap.png,"Variables residual sugar and total sulfur dioxide are redundant, but we can’t say the same for the pair density and pH.",13827
WineQT_correlation_heatmap.png,"Variables volatile acidity and alcohol are redundant, but we can’t say the same for the pair free sulfur dioxide and sulphates.",13828
WineQT_correlation_heatmap.png,"Variables chlorides and alcohol are redundant, but we can’t say the same for the pair volatile acidity and sulphates.",13829
WineQT_correlation_heatmap.png,"Variables volatile acidity and residual sugar are redundant, but we can’t say the same for the pair citric acid and sulphates.",13830
WineQT_correlation_heatmap.png,"Variables chlorides and total sulfur dioxide are redundant, but we can’t say the same for the pair citric acid and residual sugar.",13831
WineQT_correlation_heatmap.png,"Variables chlorides and sulphates are redundant, but we can’t say the same for the pair residual sugar and free sulfur dioxide.",13832
WineQT_correlation_heatmap.png,"Variables fixed acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair volatile acidity and chlorides.",13833
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair pH and alcohol.",13834
WineQT_correlation_heatmap.png,"Variables citric acid and alcohol are redundant, but we can’t say the same for the pair free sulfur dioxide and total sulfur dioxide.",13835
WineQT_correlation_heatmap.png,"Variables fixed acidity and citric acid are redundant, but we can’t say the same for the pair residual sugar and pH.",13836
WineQT_correlation_heatmap.png,"Variables volatile acidity and alcohol are redundant, but we can’t say the same for the pair free sulfur dioxide and total sulfur dioxide.",13837
WineQT_correlation_heatmap.png,"Variables chlorides and total sulfur dioxide are redundant, but we can’t say the same for the pair volatile acidity and density.",13838
WineQT_correlation_heatmap.png,"Variables density and pH are redundant, but we can’t say the same for the pair residual sugar and free sulfur dioxide.",13839
WineQT_correlation_heatmap.png,"Variables density and sulphates are redundant, but we can’t say the same for the pair volatile acidity and chlorides.",13840
WineQT_correlation_heatmap.png,"Variables pH and sulphates are redundant, but we can’t say the same for the pair chlorides and density.",13841
WineQT_correlation_heatmap.png,"Variables density and sulphates are redundant, but we can’t say the same for the pair total sulfur dioxide and alcohol.",13842
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair volatile acidity and pH.",13843
WineQT_correlation_heatmap.png,"Variables volatile acidity and sulphates are redundant, but we can’t say the same for the pair fixed acidity and density.",13844
WineQT_correlation_heatmap.png,"Variables pH and alcohol are redundant, but we can’t say the same for the pair volatile acidity and chlorides.",13845
WineQT_correlation_heatmap.png,"Variables residual sugar and pH are redundant, but we can’t say the same for the pair fixed acidity and chlorides.",13846
WineQT_correlation_heatmap.png,"Variables density and sulphates are redundant, but we can’t say the same for the pair fixed acidity and volatile acidity.",13847
WineQT_correlation_heatmap.png,"Variables fixed acidity and volatile acidity are redundant, but we can’t say the same for the pair chlorides and density.",13848
WineQT_correlation_heatmap.png,"Variables citric acid and alcohol are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",13849
WineQT_correlation_heatmap.png,"Variables fixed acidity and volatile acidity are redundant, but we can’t say the same for the pair free sulfur dioxide and sulphates.",13850
WineQT_correlation_heatmap.png,"Variables fixed acidity and density are redundant, but we can’t say the same for the pair volatile acidity and residual sugar.",13851
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair residual sugar and sulphates.",13852
WineQT_correlation_heatmap.png,"Variables residual sugar and sulphates are redundant, but we can’t say the same for the pair volatile acidity and total sulfur dioxide.",13853
WineQT_correlation_heatmap.png,"Variables chlorides and alcohol are redundant, but we can’t say the same for the pair total sulfur dioxide and density.",13854
WineQT_correlation_heatmap.png,"Variables volatile acidity and citric acid are redundant, but we can’t say the same for the pair residual sugar and density.",13855
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and pH are redundant, but we can’t say the same for the pair fixed acidity and free sulfur dioxide.",13856
WineQT_correlation_heatmap.png,"Variables chlorides and total sulfur dioxide are redundant, but we can’t say the same for the pair volatile acidity and citric acid.",13857
WineQT_correlation_heatmap.png,"Variables volatile acidity and chlorides are redundant, but we can’t say the same for the pair free sulfur dioxide and total sulfur dioxide.",13858
WineQT_correlation_heatmap.png,"Variables residual sugar and total sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and pH.",13859
WineQT_correlation_heatmap.png,"Variables residual sugar and density are redundant, but we can’t say the same for the pair sulphates and alcohol.",13860
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and pH are redundant, but we can’t say the same for the pair volatile acidity and residual sugar.",13861
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair citric acid and free sulfur dioxide.",13862
WineQT_correlation_heatmap.png,"Variables citric acid and sulphates are redundant, but we can’t say the same for the pair fixed acidity and density.",13863
WineQT_correlation_heatmap.png,"Variables citric acid and free sulfur dioxide are redundant, but we can’t say the same for the pair chlorides and total sulfur dioxide.",13864
WineQT_correlation_heatmap.png,"Variables citric acid and residual sugar are redundant, but we can’t say the same for the pair chlorides and pH.",13865
WineQT_correlation_heatmap.png,"Variables citric acid and alcohol are redundant, but we can’t say the same for the pair volatile acidity and density.",13866
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",13867
WineQT_correlation_heatmap.png,"Variables citric acid and sulphates are redundant, but we can’t say the same for the pair total sulfur dioxide and alcohol.",13868
WineQT_correlation_heatmap.png,"Variables fixed acidity and density are redundant, but we can’t say the same for the pair volatile acidity and total sulfur dioxide.",13869
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair citric acid and sulphates.",13870
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair fixed acidity and total sulfur dioxide.",13871
WineQT_correlation_heatmap.png,"Variables citric acid and sulphates are redundant, but we can’t say the same for the pair volatile acidity and chlorides.",13872
WineQT_correlation_heatmap.png,"Variables residual sugar and alcohol are redundant, but we can’t say the same for the pair chlorides and pH.",13873
WineQT_correlation_heatmap.png,"Variables volatile acidity and sulphates are redundant, but we can’t say the same for the pair citric acid and residual sugar.",13874
WineQT_correlation_heatmap.png,"Variables residual sugar and chlorides are redundant, but we can’t say the same for the pair citric acid and free sulfur dioxide.",13875
WineQT_correlation_heatmap.png,"Variables residual sugar and total sulfur dioxide are redundant, but we can’t say the same for the pair chlorides and free sulfur dioxide.",13876
WineQT_correlation_heatmap.png,"Variables chlorides and total sulfur dioxide are redundant, but we can’t say the same for the pair residual sugar and sulphates.",13877
WineQT_correlation_heatmap.png,"Variables residual sugar and total sulfur dioxide are redundant, but we can’t say the same for the pair citric acid and pH.",13878
WineQT_correlation_heatmap.png,"Variables residual sugar and density are redundant, but we can’t say the same for the pair citric acid and pH.",13879
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and total sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and residual sugar.",13880
WineQT_correlation_heatmap.png,"Variables citric acid and free sulfur dioxide are redundant, but we can’t say the same for the pair volatile acidity and total sulfur dioxide.",13881
WineQT_correlation_heatmap.png,"Variables chlorides and alcohol are redundant, but we can’t say the same for the pair volatile acidity and residual sugar.",13882
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair free sulfur dioxide and pH.",13883
WineQT_correlation_heatmap.png,"Variables volatile acidity and sulphates are redundant, but we can’t say the same for the pair residual sugar and density.",13884
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and density are redundant, but we can’t say the same for the pair volatile acidity and total sulfur dioxide.",13885
WineQT_correlation_heatmap.png,"Variables density and alcohol are redundant, but we can’t say the same for the pair volatile acidity and chlorides.",13886
WineQT_correlation_heatmap.png,"Variables volatile acidity and pH are redundant, but we can’t say the same for the pair residual sugar and free sulfur dioxide.",13887
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and pH are redundant, but we can’t say the same for the pair fixed acidity and residual sugar.",13888
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair residual sugar and alcohol.",13889
WineQT_correlation_heatmap.png,"Variables volatile acidity and pH are redundant, but we can’t say the same for the pair density and alcohol.",13890
WineQT_correlation_heatmap.png,"Variables fixed acidity and residual sugar are redundant, but we can’t say the same for the pair chlorides and density.",13891
WineQT_correlation_heatmap.png,"Variables fixed acidity and chlorides are redundant, but we can’t say the same for the pair pH and sulphates.",13892
WineQT_correlation_heatmap.png,"Variables residual sugar and pH are redundant, but we can’t say the same for the pair density and alcohol.",13893
WineQT_correlation_heatmap.png,"Variables density and pH are redundant, but we can’t say the same for the pair fixed acidity and total sulfur dioxide.",13894
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair chlorides and total sulfur dioxide.",13895
WineQT_correlation_heatmap.png,"Variables citric acid and residual sugar are redundant, but we can’t say the same for the pair density and alcohol.",13896
WineQT_correlation_heatmap.png,"Variables chlorides and total sulfur dioxide are redundant, but we can’t say the same for the pair free sulfur dioxide and alcohol.",13897
WineQT_correlation_heatmap.png,"Variables residual sugar and density are redundant, but we can’t say the same for the pair volatile acidity and total sulfur dioxide.",13898
WineQT_correlation_heatmap.png,"Variables density and pH are redundant, but we can’t say the same for the pair total sulfur dioxide and alcohol.",13899
WineQT_correlation_heatmap.png,"Variables citric acid and chlorides are redundant, but we can’t say the same for the pair free sulfur dioxide and density.",13900
WineQT_correlation_heatmap.png,"Variables pH and alcohol are redundant, but we can’t say the same for the pair chlorides and sulphates.",13901
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",13902
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and pH are redundant, but we can’t say the same for the pair residual sugar and alcohol.",13903
WineQT_correlation_heatmap.png,"Variables chlorides and density are redundant, but we can’t say the same for the pair volatile acidity and alcohol.",13904
WineQT_correlation_heatmap.png,"Variables citric acid and chlorides are redundant, but we can’t say the same for the pair total sulfur dioxide and alcohol.",13905
WineQT_correlation_heatmap.png,"Variables fixed acidity and density are redundant, but we can’t say the same for the pair total sulfur dioxide and alcohol.",13906
WineQT_correlation_heatmap.png,"Variables volatile acidity and residual sugar are redundant, but we can’t say the same for the pair density and pH.",13907
WineQT_correlation_heatmap.png,"Variables chlorides and density are redundant, but we can’t say the same for the pair fixed acidity and residual sugar.",13908
WineQT_correlation_heatmap.png,"Variables citric acid and pH are redundant, but we can’t say the same for the pair fixed acidity and sulphates.",13909
WineQT_correlation_heatmap.png,"Variables sulphates and alcohol are redundant, but we can’t say the same for the pair citric acid and free sulfur dioxide.",13910
WineQT_correlation_heatmap.png,"Variables volatile acidity and citric acid are redundant, but we can’t say the same for the pair chlorides and density.",13911
WineQT_correlation_heatmap.png,"Variables volatile acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and residual sugar.",13912
WineQT_correlation_heatmap.png,"Variables fixed acidity and density are redundant, but we can’t say the same for the pair free sulfur dioxide and pH.",13913
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair residual sugar and free sulfur dioxide.",13914
WineQT_correlation_heatmap.png,"Variables fixed acidity and sulphates are redundant, but we can’t say the same for the pair free sulfur dioxide and density.",13915
WineQT_correlation_heatmap.png,"Variables fixed acidity and residual sugar are redundant, but we can’t say the same for the pair chlorides and sulphates.",13916
WineQT_correlation_heatmap.png,"Variables chlorides and alcohol are redundant, but we can’t say the same for the pair residual sugar and total sulfur dioxide.",13917
WineQT_correlation_heatmap.png,"Variables citric acid and free sulfur dioxide are redundant, but we can’t say the same for the pair pH and alcohol.",13918
WineQT_correlation_heatmap.png,"Variables volatile acidity and pH are redundant, but we can’t say the same for the pair fixed acidity and citric acid.",13919
WineQT_correlation_heatmap.png,"Variables fixed acidity and chlorides are redundant, but we can’t say the same for the pair volatile acidity and total sulfur dioxide.",13920
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and total sulfur dioxide are redundant, but we can’t say the same for the pair volatile acidity and sulphates.",13921
WineQT_correlation_heatmap.png,"Variables volatile acidity and density are redundant, but we can’t say the same for the pair fixed acidity and sulphates.",13922
WineQT_correlation_heatmap.png,"Variables citric acid and residual sugar are redundant, but we can’t say the same for the pair volatile acidity and pH.",13923
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and total sulfur dioxide are redundant, but we can’t say the same for the pair citric acid and chlorides.",13924
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair fixed acidity and alcohol.",13925
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair fixed acidity and sulphates.",13926
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair chlorides and sulphates.",13927
WineQT_correlation_heatmap.png,"Variables fixed acidity and alcohol are redundant, but we can’t say the same for the pair volatile acidity and pH.",13928
WineQT_correlation_heatmap.png,"Variables density and sulphates are redundant, but we can’t say the same for the pair chlorides and alcohol.",13929
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and density are redundant, but we can’t say the same for the pair citric acid and pH.",13930
WineQT_correlation_heatmap.png,"Variables volatile acidity and alcohol are redundant, but we can’t say the same for the pair fixed acidity and total sulfur dioxide.",13931
WineQT_correlation_heatmap.png,"Variables chlorides and free sulfur dioxide are redundant, but we can’t say the same for the pair residual sugar and density.",13932
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and pH are redundant, but we can’t say the same for the pair citric acid and chlorides.",13933
WineQT_correlation_heatmap.png,"Variables volatile acidity and density are redundant, but we can’t say the same for the pair residual sugar and pH.",13934
WineQT_correlation_heatmap.png,"Variables volatile acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair pH and sulphates.",13935
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair free sulfur dioxide and alcohol.",13936
WineQT_correlation_heatmap.png,"Variables residual sugar and sulphates are redundant, but we can’t say the same for the pair fixed acidity and pH.",13937
WineQT_correlation_heatmap.png,"Variables citric acid and chlorides are redundant, but we can’t say the same for the pair free sulfur dioxide and alcohol.",13938
WineQT_correlation_heatmap.png,"Variables chlorides and density are redundant, but we can’t say the same for the pair fixed acidity and citric acid.",13939
WineQT_correlation_heatmap.png,"Variables volatile acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair citric acid and chlorides.",13940
WineQT_correlation_heatmap.png,"Variables volatile acidity and free sulfur dioxide are redundant, but we can’t say the same for the pair chlorides and alcohol.",13941
WineQT_correlation_heatmap.png,"Variables citric acid and pH are redundant, but we can’t say the same for the pair fixed acidity and residual sugar.",13942
WineQT_correlation_heatmap.png,"Variables volatile acidity and citric acid are redundant, but we can’t say the same for the pair total sulfur dioxide and alcohol.",13943
WineQT_correlation_heatmap.png,"Variables residual sugar and free sulfur dioxide are redundant, but we can’t say the same for the pair citric acid and chlorides.",13944
WineQT_correlation_heatmap.png,"Variables residual sugar and chlorides are redundant, but we can’t say the same for the pair pH and sulphates.",13945
WineQT_correlation_heatmap.png,"Variables fixed acidity and alcohol are redundant, but we can’t say the same for the pair citric acid and free sulfur dioxide.",13946
WineQT_correlation_heatmap.png,"Variables residual sugar and alcohol are redundant, but we can’t say the same for the pair chlorides and free sulfur dioxide.",13947
WineQT_correlation_heatmap.png,"Variables fixed acidity and citric acid are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",13948
WineQT_correlation_heatmap.png,"Variables residual sugar and chlorides are redundant, but we can’t say the same for the pair sulphates and alcohol.",13949
WineQT_correlation_heatmap.png,"Variables pH and alcohol are redundant, but we can’t say the same for the pair free sulfur dioxide and total sulfur dioxide.",13950
WineQT_correlation_heatmap.png,"Variables density and pH are redundant, but we can’t say the same for the pair fixed acidity and free sulfur dioxide.",13951
WineQT_correlation_heatmap.png,"Variables sulphates and alcohol are redundant, but we can’t say the same for the pair volatile acidity and pH.",13952
WineQT_correlation_heatmap.png,"Variables volatile acidity and pH are redundant, but we can’t say the same for the pair citric acid and density.",13953
WineQT_correlation_heatmap.png,"Variables residual sugar and alcohol are redundant, but we can’t say the same for the pair density and pH.",13954
WineQT_correlation_heatmap.png,"Variables citric acid and free sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and residual sugar.",13955
WineQT_correlation_heatmap.png,"Variables density and sulphates are redundant, but we can’t say the same for the pair residual sugar and total sulfur dioxide.",13956
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and alcohol are redundant, but we can’t say the same for the pair chlorides and pH.",13957
WineQT_correlation_heatmap.png,"Variables fixed acidity and sulphates are redundant, but we can’t say the same for the pair citric acid and total sulfur dioxide.",13958
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair density and alcohol.",13959
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair volatile acidity and density.",13960
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair volatile acidity and density.",13961
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and density are redundant, but we can’t say the same for the pair pH and sulphates.",13962
WineQT_correlation_heatmap.png,"Variables volatile acidity and pH are redundant, but we can’t say the same for the pair citric acid and sulphates.",13963
WineQT_correlation_heatmap.png,"Variables fixed acidity and residual sugar are redundant, but we can’t say the same for the pair total sulfur dioxide and alcohol.",13964
WineQT_correlation_heatmap.png,"Variables volatile acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair chlorides and sulphates.",13965
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and alcohol are redundant, but we can’t say the same for the pair citric acid and sulphates.",13966
WineQT_correlation_heatmap.png,"Variables chlorides and free sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and residual sugar.",13967
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and density are redundant, but we can’t say the same for the pair pH and alcohol.",13968
WineQT_correlation_heatmap.png,"Variables sulphates and alcohol are redundant, but we can’t say the same for the pair citric acid and total sulfur dioxide.",13969
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and density are redundant, but we can’t say the same for the pair citric acid and residual sugar.",13970
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and pH are redundant, but we can’t say the same for the pair fixed acidity and sulphates.",13971
WineQT_correlation_heatmap.png,"Variables density and alcohol are redundant, but we can’t say the same for the pair citric acid and total sulfur dioxide.",13972
WineQT_correlation_heatmap.png,"Variables density and sulphates are redundant, but we can’t say the same for the pair residual sugar and alcohol.",13973
WineQT_correlation_heatmap.png,"Variables citric acid and alcohol are redundant, but we can’t say the same for the pair chlorides and density.",13974
WineQT_correlation_heatmap.png,"Variables volatile acidity and alcohol are redundant, but we can’t say the same for the pair fixed acidity and chlorides.",13975
WineQT_correlation_heatmap.png,"Variables fixed acidity and volatile acidity are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",13976
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair fixed acidity and total sulfur dioxide.",13977
WineQT_correlation_heatmap.png,"Variables volatile acidity and chlorides are redundant, but we can’t say the same for the pair fixed acidity and alcohol.",13978
WineQT_correlation_heatmap.png,"Variables fixed acidity and volatile acidity are redundant, but we can’t say the same for the pair residual sugar and alcohol.",13979
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and pH are redundant, but we can’t say the same for the pair chlorides and sulphates.",13980
WineQT_correlation_heatmap.png,"Variables citric acid and free sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and volatile acidity.",13981
WineQT_correlation_heatmap.png,"Variables volatile acidity and residual sugar are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",13982
WineQT_correlation_heatmap.png,"Variables residual sugar and sulphates are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",13983
WineQT_correlation_heatmap.png,"Variables volatile acidity and citric acid are redundant, but we can’t say the same for the pair chlorides and alcohol.",13984
WineQT_correlation_heatmap.png,"Variables citric acid and sulphates are redundant, but we can’t say the same for the pair chlorides and total sulfur dioxide.",13985
WineQT_correlation_heatmap.png,"Variables residual sugar and sulphates are redundant, but we can’t say the same for the pair fixed acidity and density.",13986
WineQT_correlation_heatmap.png,"Variables volatile acidity and free sulfur dioxide are redundant, but we can’t say the same for the pair citric acid and sulphates.",13987
WineQT_correlation_heatmap.png,"Variables density and alcohol are redundant, but we can’t say the same for the pair volatile acidity and free sulfur dioxide.",13988
WineQT_correlation_heatmap.png,"Variables volatile acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and free sulfur dioxide.",13989
WineQT_correlation_heatmap.png,"Variables fixed acidity and residual sugar are redundant, but we can’t say the same for the pair density and alcohol.",13990
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair residual sugar and chlorides.",13991
WineQT_correlation_heatmap.png,"Variables pH and alcohol are redundant, but we can’t say the same for the pair citric acid and chlorides.",13992
WineQT_correlation_heatmap.png,"Variables volatile acidity and total sulfur dioxide are redundant, but we can’t say the same for the pair fixed acidity and citric acid.",13993
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and density are redundant, but we can’t say the same for the pair volatile acidity and pH.",13994
WineQT_correlation_heatmap.png,"Variables fixed acidity and alcohol are redundant, but we can’t say the same for the pair volatile acidity and sulphates.",13995
WineQT_correlation_heatmap.png,"Variables fixed acidity and pH are redundant, but we can’t say the same for the pair free sulfur dioxide and total sulfur dioxide.",13996
WineQT_correlation_heatmap.png,"Variables fixed acidity and density are redundant, but we can’t say the same for the pair volatile acidity and free sulfur dioxide.",13997
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and density are redundant, but we can’t say the same for the pair fixed acidity and sulphates.",13998
WineQT_correlation_heatmap.png,"Variables volatile acidity and density are redundant, but we can’t say the same for the pair chlorides and free sulfur dioxide.",13999
WineQT_correlation_heatmap.png,"Variables volatile acidity and chlorides are redundant, but we can’t say the same for the pair free sulfur dioxide and density.",14000
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair total sulfur dioxide and density.",14001
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and density are redundant, but we can’t say the same for the pair residual sugar and sulphates.",14002
WineQT_correlation_heatmap.png,"Variables chlorides and pH are redundant, but we can’t say the same for the pair volatile acidity and residual sugar.",14003
WineQT_correlation_heatmap.png,"Variables pH and sulphates are redundant, but we can’t say the same for the pair fixed acidity and alcohol.",14004
WineQT_correlation_heatmap.png,"Variables volatile acidity and chlorides are redundant, but we can’t say the same for the pair density and alcohol.",14005
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and density are redundant, but we can’t say the same for the pair chlorides and free sulfur dioxide.",14006
WineQT_correlation_heatmap.png,"Variables residual sugar and pH are redundant, but we can’t say the same for the pair volatile acidity and density.",14007
WineQT_correlation_heatmap.png,"Variables chlorides and free sulfur dioxide are redundant, but we can’t say the same for the pair volatile acidity and sulphates.",14008
WineQT_correlation_heatmap.png,"Variables citric acid and chlorides are redundant, but we can’t say the same for the pair total sulfur dioxide and pH.",14009
WineQT_correlation_heatmap.png,"Variables pH and sulphates are redundant, but we can’t say the same for the pair residual sugar and density.",14010
WineQT_correlation_heatmap.png,"Variables free sulfur dioxide and alcohol are redundant, but we can’t say the same for the pair citric acid and total sulfur dioxide.",14011
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and sulphates are redundant, but we can’t say the same for the pair free sulfur dioxide and density.",14012
WineQT_correlation_heatmap.png,"Variables total sulfur dioxide and density are redundant, but we can’t say the same for the pair pH and alcohol.",14013
WineQT_correlation_heatmap.png,"Variables pH and sulphates are redundant, but we can’t say the same for the pair volatile acidity and density.",14014
WineQT_correlation_heatmap.png,"Variables citric acid and density are redundant, but we can’t say the same for the pair volatile acidity and chlorides.",14015
WineQT_correlation_heatmap.png,"Variables pH and alcohol are redundant, but we can’t say the same for the pair citric acid and free sulfur dioxide.",14016
WineQT_correlation_heatmap.png,"Variables fixed acidity and density are redundant, but we can’t say the same for the pair volatile acidity and pH.",14017
WineQT_correlation_heatmap.png,"Variables citric acid and sulphates are redundant, but we can’t say the same for the pair fixed acidity and free sulfur dioxide.",14018
WineQT_correlation_heatmap.png,The variable fixed acidity can be discarded without risking losing information.,14019
WineQT_correlation_heatmap.png,The variable volatile acidity can be discarded without risking losing information.,14020
WineQT_correlation_heatmap.png,The variable citric acid can be discarded without risking losing information.,14021
WineQT_correlation_heatmap.png,The variable residual sugar can be discarded without risking losing information.,14022
WineQT_correlation_heatmap.png,The variable chlorides can be discarded without risking losing information.,14023
WineQT_correlation_heatmap.png,The variable free sulfur dioxide can be discarded without risking losing information.,14024
WineQT_correlation_heatmap.png,The variable total sulfur dioxide can be discarded without risking losing information.,14025
WineQT_correlation_heatmap.png,The variable density can be discarded without risking losing information.,14026
WineQT_correlation_heatmap.png,The variable pH can be discarded without risking losing information.,14027
WineQT_correlation_heatmap.png,The variable sulphates can be discarded without risking losing information.,14028
WineQT_correlation_heatmap.png,The variable alcohol can be discarded without risking losing information.,14029
WineQT_correlation_heatmap.png,The variable quality can be discarded without risking losing information.,14030
WineQT_correlation_heatmap.png,One of the variables volatile acidity or fixed acidity can be discarded without losing information.,14031
WineQT_correlation_heatmap.png,One of the variables citric acid or fixed acidity can be discarded without losing information.,14032
WineQT_correlation_heatmap.png,One of the variables residual sugar or fixed acidity can be discarded without losing information.,14033
WineQT_correlation_heatmap.png,One of the variables chlorides or fixed acidity can be discarded without losing information.,14034
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or fixed acidity can be discarded without losing information.,14035
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or fixed acidity can be discarded without losing information.,14036
WineQT_correlation_heatmap.png,One of the variables density or fixed acidity can be discarded without losing information.,14037
WineQT_correlation_heatmap.png,One of the variables pH or fixed acidity can be discarded without losing information.,14038
WineQT_correlation_heatmap.png,One of the variables sulphates or fixed acidity can be discarded without losing information.,14039
WineQT_correlation_heatmap.png,One of the variables alcohol or fixed acidity can be discarded without losing information.,14040
WineQT_correlation_heatmap.png,One of the variables quality or fixed acidity can be discarded without losing information.,14041
WineQT_correlation_heatmap.png,One of the variables fixed acidity or volatile acidity can be discarded without losing information.,14042
WineQT_correlation_heatmap.png,One of the variables citric acid or volatile acidity can be discarded without losing information.,14043
WineQT_correlation_heatmap.png,One of the variables residual sugar or volatile acidity can be discarded without losing information.,14044
WineQT_correlation_heatmap.png,One of the variables chlorides or volatile acidity can be discarded without losing information.,14045
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or volatile acidity can be discarded without losing information.,14046
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or volatile acidity can be discarded without losing information.,14047
WineQT_correlation_heatmap.png,One of the variables density or volatile acidity can be discarded without losing information.,14048
WineQT_correlation_heatmap.png,One of the variables pH or volatile acidity can be discarded without losing information.,14049
WineQT_correlation_heatmap.png,One of the variables sulphates or volatile acidity can be discarded without losing information.,14050
WineQT_correlation_heatmap.png,One of the variables alcohol or volatile acidity can be discarded without losing information.,14051
WineQT_correlation_heatmap.png,One of the variables quality or volatile acidity can be discarded without losing information.,14052
WineQT_correlation_heatmap.png,One of the variables fixed acidity or citric acid can be discarded without losing information.,14053
WineQT_correlation_heatmap.png,One of the variables volatile acidity or citric acid can be discarded without losing information.,14054
WineQT_correlation_heatmap.png,One of the variables residual sugar or citric acid can be discarded without losing information.,14055
WineQT_correlation_heatmap.png,One of the variables chlorides or citric acid can be discarded without losing information.,14056
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or citric acid can be discarded without losing information.,14057
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or citric acid can be discarded without losing information.,14058
WineQT_correlation_heatmap.png,One of the variables density or citric acid can be discarded without losing information.,14059
WineQT_correlation_heatmap.png,One of the variables pH or citric acid can be discarded without losing information.,14060
WineQT_correlation_heatmap.png,One of the variables sulphates or citric acid can be discarded without losing information.,14061
WineQT_correlation_heatmap.png,One of the variables alcohol or citric acid can be discarded without losing information.,14062
WineQT_correlation_heatmap.png,One of the variables quality or citric acid can be discarded without losing information.,14063
WineQT_correlation_heatmap.png,One of the variables fixed acidity or residual sugar can be discarded without losing information.,14064
WineQT_correlation_heatmap.png,One of the variables volatile acidity or residual sugar can be discarded without losing information.,14065
WineQT_correlation_heatmap.png,One of the variables citric acid or residual sugar can be discarded without losing information.,14066
WineQT_correlation_heatmap.png,One of the variables chlorides or residual sugar can be discarded without losing information.,14067
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or residual sugar can be discarded without losing information.,14068
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or residual sugar can be discarded without losing information.,14069
WineQT_correlation_heatmap.png,One of the variables density or residual sugar can be discarded without losing information.,14070
WineQT_correlation_heatmap.png,One of the variables pH or residual sugar can be discarded without losing information.,14071
WineQT_correlation_heatmap.png,One of the variables sulphates or residual sugar can be discarded without losing information.,14072
WineQT_correlation_heatmap.png,One of the variables alcohol or residual sugar can be discarded without losing information.,14073
WineQT_correlation_heatmap.png,One of the variables quality or residual sugar can be discarded without losing information.,14074
WineQT_correlation_heatmap.png,One of the variables fixed acidity or chlorides can be discarded without losing information.,14075
WineQT_correlation_heatmap.png,One of the variables volatile acidity or chlorides can be discarded without losing information.,14076
WineQT_correlation_heatmap.png,One of the variables citric acid or chlorides can be discarded without losing information.,14077
WineQT_correlation_heatmap.png,One of the variables residual sugar or chlorides can be discarded without losing information.,14078
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or chlorides can be discarded without losing information.,14079
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or chlorides can be discarded without losing information.,14080
WineQT_correlation_heatmap.png,One of the variables density or chlorides can be discarded without losing information.,14081
WineQT_correlation_heatmap.png,One of the variables pH or chlorides can be discarded without losing information.,14082
WineQT_correlation_heatmap.png,One of the variables sulphates or chlorides can be discarded without losing information.,14083
WineQT_correlation_heatmap.png,One of the variables alcohol or chlorides can be discarded without losing information.,14084
WineQT_correlation_heatmap.png,One of the variables quality or chlorides can be discarded without losing information.,14085
WineQT_correlation_heatmap.png,One of the variables fixed acidity or free sulfur dioxide can be discarded without losing information.,14086
WineQT_correlation_heatmap.png,One of the variables volatile acidity or free sulfur dioxide can be discarded without losing information.,14087
WineQT_correlation_heatmap.png,One of the variables citric acid or free sulfur dioxide can be discarded without losing information.,14088
WineQT_correlation_heatmap.png,One of the variables residual sugar or free sulfur dioxide can be discarded without losing information.,14089
WineQT_correlation_heatmap.png,One of the variables chlorides or free sulfur dioxide can be discarded without losing information.,14090
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or free sulfur dioxide can be discarded without losing information.,14091
WineQT_correlation_heatmap.png,One of the variables density or free sulfur dioxide can be discarded without losing information.,14092
WineQT_correlation_heatmap.png,One of the variables pH or free sulfur dioxide can be discarded without losing information.,14093
WineQT_correlation_heatmap.png,One of the variables sulphates or free sulfur dioxide can be discarded without losing information.,14094
WineQT_correlation_heatmap.png,One of the variables alcohol or free sulfur dioxide can be discarded without losing information.,14095
WineQT_correlation_heatmap.png,One of the variables quality or free sulfur dioxide can be discarded without losing information.,14096
WineQT_correlation_heatmap.png,One of the variables fixed acidity or total sulfur dioxide can be discarded without losing information.,14097
WineQT_correlation_heatmap.png,One of the variables volatile acidity or total sulfur dioxide can be discarded without losing information.,14098
WineQT_correlation_heatmap.png,One of the variables citric acid or total sulfur dioxide can be discarded without losing information.,14099
WineQT_correlation_heatmap.png,One of the variables residual sugar or total sulfur dioxide can be discarded without losing information.,14100
WineQT_correlation_heatmap.png,One of the variables chlorides or total sulfur dioxide can be discarded without losing information.,14101
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or total sulfur dioxide can be discarded without losing information.,14102
WineQT_correlation_heatmap.png,One of the variables density or total sulfur dioxide can be discarded without losing information.,14103
WineQT_correlation_heatmap.png,One of the variables pH or total sulfur dioxide can be discarded without losing information.,14104
WineQT_correlation_heatmap.png,One of the variables sulphates or total sulfur dioxide can be discarded without losing information.,14105
WineQT_correlation_heatmap.png,One of the variables alcohol or total sulfur dioxide can be discarded without losing information.,14106
WineQT_correlation_heatmap.png,One of the variables quality or total sulfur dioxide can be discarded without losing information.,14107
WineQT_correlation_heatmap.png,One of the variables fixed acidity or density can be discarded without losing information.,14108
WineQT_correlation_heatmap.png,One of the variables volatile acidity or density can be discarded without losing information.,14109
WineQT_correlation_heatmap.png,One of the variables citric acid or density can be discarded without losing information.,14110
WineQT_correlation_heatmap.png,One of the variables residual sugar or density can be discarded without losing information.,14111
WineQT_correlation_heatmap.png,One of the variables chlorides or density can be discarded without losing information.,14112
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or density can be discarded without losing information.,14113
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or density can be discarded without losing information.,14114
WineQT_correlation_heatmap.png,One of the variables pH or density can be discarded without losing information.,14115
WineQT_correlation_heatmap.png,One of the variables sulphates or density can be discarded without losing information.,14116
WineQT_correlation_heatmap.png,One of the variables alcohol or density can be discarded without losing information.,14117
WineQT_correlation_heatmap.png,One of the variables quality or density can be discarded without losing information.,14118
WineQT_correlation_heatmap.png,One of the variables fixed acidity or pH can be discarded without losing information.,14119
WineQT_correlation_heatmap.png,One of the variables volatile acidity or pH can be discarded without losing information.,14120
WineQT_correlation_heatmap.png,One of the variables citric acid or pH can be discarded without losing information.,14121
WineQT_correlation_heatmap.png,One of the variables residual sugar or pH can be discarded without losing information.,14122
WineQT_correlation_heatmap.png,One of the variables chlorides or pH can be discarded without losing information.,14123
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or pH can be discarded without losing information.,14124
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or pH can be discarded without losing information.,14125
WineQT_correlation_heatmap.png,One of the variables density or pH can be discarded without losing information.,14126
WineQT_correlation_heatmap.png,One of the variables sulphates or pH can be discarded without losing information.,14127
WineQT_correlation_heatmap.png,One of the variables alcohol or pH can be discarded without losing information.,14128
WineQT_correlation_heatmap.png,One of the variables quality or pH can be discarded without losing information.,14129
WineQT_correlation_heatmap.png,One of the variables fixed acidity or sulphates can be discarded without losing information.,14130
WineQT_correlation_heatmap.png,One of the variables volatile acidity or sulphates can be discarded without losing information.,14131
WineQT_correlation_heatmap.png,One of the variables citric acid or sulphates can be discarded without losing information.,14132
WineQT_correlation_heatmap.png,One of the variables residual sugar or sulphates can be discarded without losing information.,14133
WineQT_correlation_heatmap.png,One of the variables chlorides or sulphates can be discarded without losing information.,14134
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or sulphates can be discarded without losing information.,14135
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or sulphates can be discarded without losing information.,14136
WineQT_correlation_heatmap.png,One of the variables density or sulphates can be discarded without losing information.,14137
WineQT_correlation_heatmap.png,One of the variables pH or sulphates can be discarded without losing information.,14138
WineQT_correlation_heatmap.png,One of the variables alcohol or sulphates can be discarded without losing information.,14139
WineQT_correlation_heatmap.png,One of the variables quality or sulphates can be discarded without losing information.,14140
WineQT_correlation_heatmap.png,One of the variables fixed acidity or alcohol can be discarded without losing information.,14141
WineQT_correlation_heatmap.png,One of the variables volatile acidity or alcohol can be discarded without losing information.,14142
WineQT_correlation_heatmap.png,One of the variables citric acid or alcohol can be discarded without losing information.,14143
WineQT_correlation_heatmap.png,One of the variables residual sugar or alcohol can be discarded without losing information.,14144
WineQT_correlation_heatmap.png,One of the variables chlorides or alcohol can be discarded without losing information.,14145
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or alcohol can be discarded without losing information.,14146
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or alcohol can be discarded without losing information.,14147
WineQT_correlation_heatmap.png,One of the variables density or alcohol can be discarded without losing information.,14148
WineQT_correlation_heatmap.png,One of the variables pH or alcohol can be discarded without losing information.,14149
WineQT_correlation_heatmap.png,One of the variables sulphates or alcohol can be discarded without losing information.,14150
WineQT_correlation_heatmap.png,One of the variables quality or alcohol can be discarded without losing information.,14151
WineQT_correlation_heatmap.png,One of the variables fixed acidity or quality can be discarded without losing information.,14152
WineQT_correlation_heatmap.png,One of the variables volatile acidity or quality can be discarded without losing information.,14153
WineQT_correlation_heatmap.png,One of the variables citric acid or quality can be discarded without losing information.,14154
WineQT_correlation_heatmap.png,One of the variables residual sugar or quality can be discarded without losing information.,14155
WineQT_correlation_heatmap.png,One of the variables chlorides or quality can be discarded without losing information.,14156
WineQT_correlation_heatmap.png,One of the variables free sulfur dioxide or quality can be discarded without losing information.,14157
WineQT_correlation_heatmap.png,One of the variables total sulfur dioxide or quality can be discarded without losing information.,14158
WineQT_correlation_heatmap.png,One of the variables density or quality can be discarded without losing information.,14159
WineQT_correlation_heatmap.png,One of the variables pH or quality can be discarded without losing information.,14160
WineQT_correlation_heatmap.png,One of the variables sulphates or quality can be discarded without losing information.,14161
WineQT_correlation_heatmap.png,One of the variables alcohol or quality can be discarded without losing information.,14162
WineQT_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,14163
WineQT_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,14164
WineQT_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,14165
WineQT_histograms_numeric.png,At least 50 of the variables present outliers.,14166
WineQT_histograms_numeric.png,At least 60 of the variables present outliers.,14167
WineQT_histograms_numeric.png,At least 75 of the variables present outliers.,14168
WineQT_histograms_numeric.png,At least 85 of the variables present outliers.,14169
WineQT_histograms_numeric.png,Variable fixed acidity presents some outliers.,14170
WineQT_boxplots.png,Variable volatile acidity presents some outliers.,14171
WineQT_histograms_numeric.png,Variable citric acid presents some outliers.,14172
WineQT_boxplots.png,Variable residual sugar presents some outliers.,14173
WineQT_boxplots.png,Variable chlorides presents some outliers.,14174
WineQT_boxplots.png,Variable free sulfur dioxide presents some outliers.,14175
WineQT_boxplots.png,Variable total sulfur dioxide presents some outliers.,14176
WineQT_histograms_numeric.png,Variable density presents some outliers.,14177
WineQT_boxplots.png,Variable pH presents some outliers.,14178
WineQT_boxplots.png,Variable sulphates presents some outliers.,14179
WineQT_histograms_numeric.png,Variable alcohol presents some outliers.,14180
WineQT_boxplots.png,Variable quality presents some outliers.,14181
WineQT_boxplots.png,Variable fixed acidity doesn’t have any outliers.,14182
WineQT_histograms_numeric.png,Variable volatile acidity doesn’t have any outliers.,14183
WineQT_histograms_numeric.png,Variable citric acid doesn’t have any outliers.,14184
WineQT_histograms_numeric.png,Variable residual sugar doesn’t have any outliers.,14185
WineQT_boxplots.png,Variable chlorides doesn’t have any outliers.,14186
WineQT_histograms_numeric.png,Variable free sulfur dioxide doesn’t have any outliers.,14187
WineQT_boxplots.png,Variable total sulfur dioxide doesn’t have any outliers.,14188
WineQT_boxplots.png,Variable density doesn’t have any outliers.,14189
WineQT_boxplots.png,Variable pH doesn’t have any outliers.,14190
WineQT_histograms_numeric.png,Variable sulphates doesn’t have any outliers.,14191
WineQT_boxplots.png,Variable alcohol doesn’t have any outliers.,14192
WineQT_histograms_numeric.png,Variable quality doesn’t have any outliers.,14193
WineQT_histograms_numeric.png,Variable fixed acidity shows some outlier values.,14194
WineQT_boxplots.png,Variable volatile acidity shows some outlier values.,14195
WineQT_boxplots.png,Variable citric acid shows some outlier values.,14196
WineQT_boxplots.png,Variable residual sugar shows some outlier values.,14197
WineQT_boxplots.png,Variable chlorides shows some outlier values.,14198
WineQT_boxplots.png,Variable free sulfur dioxide shows some outlier values.,14199
WineQT_boxplots.png,Variable total sulfur dioxide shows some outlier values.,14200
WineQT_boxplots.png,Variable density shows some outlier values.,14201
WineQT_boxplots.png,Variable pH shows some outlier values.,14202
WineQT_histograms_numeric.png,Variable sulphates shows some outlier values.,14203
WineQT_histograms_numeric.png,Variable alcohol shows some outlier values.,14204
WineQT_histograms_numeric.png,Variable quality shows some outlier values.,14205
WineQT_boxplots.png,Variable fixed acidity shows a high number of outlier values.,14206
WineQT_histograms_numeric.png,Variable volatile acidity shows a high number of outlier values.,14207
WineQT_boxplots.png,Variable citric acid shows a high number of outlier values.,14208
WineQT_histograms_numeric.png,Variable residual sugar shows a high number of outlier values.,14209
WineQT_histograms_numeric.png,Variable chlorides shows a high number of outlier values.,14210
WineQT_histograms_numeric.png,Variable free sulfur dioxide shows a high number of outlier values.,14211
WineQT_boxplots.png,Variable total sulfur dioxide shows a high number of outlier values.,14212
WineQT_histograms_numeric.png,Variable density shows a high number of outlier values.,14213
WineQT_boxplots.png,Variable pH shows a high number of outlier values.,14214
WineQT_histograms_numeric.png,Variable sulphates shows a high number of outlier values.,14215
WineQT_histograms_numeric.png,Variable alcohol shows a high number of outlier values.,14216
WineQT_histograms_numeric.png,Variable quality shows a high number of outlier values.,14217
WineQT_histograms_numeric.png,Outliers seem to be a problem in the dataset.,14218
WineQT_boxplots.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14219
WineQT_boxplots.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14220
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14221
WineQT_histograms_numeric.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14222
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14223
WineQT_boxplots.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14224
WineQT_boxplots.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14225
WineQT_histograms_numeric.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14226
WineQT_histograms_numeric.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14227
WineQT_histograms_numeric.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14228
WineQT_boxplots.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable fixed acidity.",14229
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14230
WineQT_histograms_numeric.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14231
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14232
WineQT_boxplots.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14233
WineQT_boxplots.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14234
WineQT_histograms_numeric.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14235
WineQT_histograms_numeric.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14236
WineQT_boxplots.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14237
WineQT_histograms_numeric.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14238
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14239
WineQT_histograms_numeric.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable volatile acidity.",14240
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable citric acid.",14241
WineQT_boxplots.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable citric acid.",14242
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable citric acid.",14243
WineQT_histograms_numeric.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable citric acid.",14244
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable citric acid.",14245
WineQT_boxplots.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable citric acid.",14246
WineQT_histograms_numeric.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable citric acid.",14247
WineQT_histograms_numeric.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable citric acid.",14248
WineQT_boxplots.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable citric acid.",14249
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable citric acid.",14250
WineQT_boxplots.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable citric acid.",14251
WineQT_histograms_numeric.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable residual sugar.",14252
WineQT_boxplots.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable residual sugar.",14253
WineQT_boxplots.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable residual sugar.",14254
WineQT_histograms_numeric.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable residual sugar.",14255
WineQT_boxplots.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable residual sugar.",14256
WineQT_boxplots.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable residual sugar.",14257
WineQT_histograms_numeric.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable residual sugar.",14258
WineQT_boxplots.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable residual sugar.",14259
WineQT_histograms_numeric.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable residual sugar.",14260
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable residual sugar.",14261
WineQT_histograms_numeric.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable residual sugar.",14262
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable chlorides.",14263
WineQT_histograms_numeric.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable chlorides.",14264
WineQT_histograms_numeric.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable chlorides.",14265
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable chlorides.",14266
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable chlorides.",14267
WineQT_histograms_numeric.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable chlorides.",14268
WineQT_histograms_numeric.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable chlorides.",14269
WineQT_histograms_numeric.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable chlorides.",14270
WineQT_histograms_numeric.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable chlorides.",14271
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable chlorides.",14272
WineQT_boxplots.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable chlorides.",14273
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14274
WineQT_histograms_numeric.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14275
WineQT_histograms_numeric.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14276
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14277
WineQT_histograms_numeric.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14278
WineQT_histograms_numeric.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14279
WineQT_boxplots.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14280
WineQT_boxplots.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14281
WineQT_boxplots.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14282
WineQT_histograms_numeric.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14283
WineQT_histograms_numeric.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable free sulfur dioxide.",14284
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14285
WineQT_histograms_numeric.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14286
WineQT_histograms_numeric.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14287
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14288
WineQT_histograms_numeric.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14289
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14290
WineQT_histograms_numeric.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14291
WineQT_histograms_numeric.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14292
WineQT_histograms_numeric.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14293
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14294
WineQT_histograms_numeric.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable total sulfur dioxide.",14295
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable density.",14296
WineQT_boxplots.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable density.",14297
WineQT_boxplots.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable density.",14298
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable density.",14299
WineQT_histograms_numeric.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable density.",14300
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable density.",14301
WineQT_histograms_numeric.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable density.",14302
WineQT_boxplots.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable density.",14303
WineQT_boxplots.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable density.",14304
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable density.",14305
WineQT_boxplots.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable density.",14306
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable pH.",14307
WineQT_histograms_numeric.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable pH.",14308
WineQT_histograms_numeric.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable pH.",14309
WineQT_histograms_numeric.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable pH.",14310
WineQT_boxplots.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable pH.",14311
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable pH.",14312
WineQT_boxplots.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable pH.",14313
WineQT_boxplots.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable pH.",14314
WineQT_boxplots.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable pH.",14315
WineQT_histograms_numeric.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable pH.",14316
WineQT_histograms_numeric.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable pH.",14317
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable sulphates.",14318
WineQT_histograms_numeric.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable sulphates.",14319
WineQT_histograms_numeric.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable sulphates.",14320
WineQT_histograms_numeric.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable sulphates.",14321
WineQT_boxplots.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable sulphates.",14322
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable sulphates.",14323
WineQT_histograms_numeric.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable sulphates.",14324
WineQT_boxplots.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable sulphates.",14325
WineQT_histograms_numeric.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable sulphates.",14326
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable sulphates.",14327
WineQT_histograms_numeric.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable sulphates.",14328
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable alcohol.",14329
WineQT_histograms_numeric.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable alcohol.",14330
WineQT_histograms_numeric.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable alcohol.",14331
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable alcohol.",14332
WineQT_histograms_numeric.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable alcohol.",14333
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable alcohol.",14334
WineQT_boxplots.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable alcohol.",14335
WineQT_boxplots.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable alcohol.",14336
WineQT_boxplots.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable alcohol.",14337
WineQT_boxplots.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable alcohol.",14338
WineQT_boxplots.png,"It is clear that variable quality shows some outliers, but we can’t be sure of the same for variable alcohol.",14339
WineQT_boxplots.png,"It is clear that variable fixed acidity shows some outliers, but we can’t be sure of the same for variable quality.",14340
WineQT_histograms_numeric.png,"It is clear that variable volatile acidity shows some outliers, but we can’t be sure of the same for variable quality.",14341
WineQT_boxplots.png,"It is clear that variable citric acid shows some outliers, but we can’t be sure of the same for variable quality.",14342
WineQT_boxplots.png,"It is clear that variable residual sugar shows some outliers, but we can’t be sure of the same for variable quality.",14343
WineQT_boxplots.png,"It is clear that variable chlorides shows some outliers, but we can’t be sure of the same for variable quality.",14344
WineQT_histograms_numeric.png,"It is clear that variable free sulfur dioxide shows some outliers, but we can’t be sure of the same for variable quality.",14345
WineQT_boxplots.png,"It is clear that variable total sulfur dioxide shows some outliers, but we can’t be sure of the same for variable quality.",14346
WineQT_histograms_numeric.png,"It is clear that variable density shows some outliers, but we can’t be sure of the same for variable quality.",14347
WineQT_histograms_numeric.png,"It is clear that variable pH shows some outliers, but we can’t be sure of the same for variable quality.",14348
WineQT_boxplots.png,"It is clear that variable sulphates shows some outliers, but we can’t be sure of the same for variable quality.",14349
WineQT_boxplots.png,"It is clear that variable alcohol shows some outliers, but we can’t be sure of the same for variable quality.",14350
WineQT_boxplots.png,Those boxplots show that the data is not normalized.,14351
WineQT_boxplots.png,Variable fixed acidity is balanced.,14352
WineQT_boxplots.png,Variable volatile acidity is balanced.,14353
WineQT_histograms_numeric.png,Variable citric acid is balanced.,14354
WineQT_boxplots.png,Variable residual sugar is balanced.,14355
WineQT_boxplots.png,Variable chlorides is balanced.,14356
WineQT_boxplots.png,Variable free sulfur dioxide is balanced.,14357
WineQT_histograms_numeric.png,Variable total sulfur dioxide is balanced.,14358
WineQT_boxplots.png,Variable density is balanced.,14359
WineQT_histograms_numeric.png,Variable pH is balanced.,14360
WineQT_histograms_numeric.png,Variable sulphates is balanced.,14361
WineQT_histograms_numeric.png,Variable alcohol is balanced.,14362
WineQT_boxplots.png,Variable quality is balanced.,14363
WineQT_histograms.png,The variable fixed acidity can be seen as ordinal without losing information.,14364
WineQT_histograms.png,The variable volatile acidity can be seen as ordinal without losing information.,14365
WineQT_histograms.png,The variable citric acid can be seen as ordinal without losing information.,14366
WineQT_histograms.png,The variable residual sugar can be seen as ordinal without losing information.,14367
WineQT_histograms.png,The variable chlorides can be seen as ordinal without losing information.,14368
WineQT_histograms.png,The variable free sulfur dioxide can be seen as ordinal without losing information.,14369
WineQT_histograms.png,The variable total sulfur dioxide can be seen as ordinal without losing information.,14370
WineQT_histograms.png,The variable density can be seen as ordinal without losing information.,14371
WineQT_histograms.png,The variable pH can be seen as ordinal without losing information.,14372
WineQT_histograms.png,The variable sulphates can be seen as ordinal without losing information.,14373
WineQT_histograms.png,The variable alcohol can be seen as ordinal without losing information.,14374
WineQT_histograms.png,The variable fixed acidity can be seen as ordinal.,14375
WineQT_histograms.png,The variable volatile acidity can be seen as ordinal.,14376
WineQT_histograms.png,The variable citric acid can be seen as ordinal.,14377
WineQT_histograms.png,The variable residual sugar can be seen as ordinal.,14378
WineQT_histograms.png,The variable chlorides can be seen as ordinal.,14379
WineQT_histograms.png,The variable free sulfur dioxide can be seen as ordinal.,14380
WineQT_histograms.png,The variable total sulfur dioxide can be seen as ordinal.,14381
WineQT_histograms.png,The variable density can be seen as ordinal.,14382
WineQT_histograms.png,The variable pH can be seen as ordinal.,14383
WineQT_histograms.png,The variable sulphates can be seen as ordinal.,14384
WineQT_histograms.png,The variable alcohol can be seen as ordinal.,14385
WineQT_histograms.png,"All variables, but the class, should be dealt with as numeric.",14386
WineQT_histograms.png,"All variables, but the class, should be dealt with as binary.",14387
WineQT_histograms.png,"All variables, but the class, should be dealt with as date.",14388
WineQT_histograms.png,"All variables, but the class, should be dealt with as symbolic.",14389
WineQT_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 56.,14390
WineQT_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 79.,14391
WineQT_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 40.,14392
WineQT_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 63.,14393
WineQT_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 65.,14394
WineQT_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,14395
WineQT_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",14396
WineQT_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",14397
WineQT_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",14398
WineQT_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",14399
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 0.",14400
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 0.",14401
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 0.",14402
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 0.",14403
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",14404
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",14405
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",14406
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",14407
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 181.",14408
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 181.",14409
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 181.",14410
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 181.",14411
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 181.",14412
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 181.",14413
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 181.",14414
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 181.",14415
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 72.",14416
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 72.",14417
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 72.",14418
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 72.",14419
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 72.",14420
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 72.",14421
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 72.",14422
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 72.",14423
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 188.",14424
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 188.",14425
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 188.",14426
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 188.",14427
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 188.",14428
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 188.",14429
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 188.",14430
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 188.",14431
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 57.",14432
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 57.",14433
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 57.",14434
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 57.",14435
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 57.",14436
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 57.",14437
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 57.",14438
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 57.",14439
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (A,B) as 0.",14440
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (not A, B) as 0.",14441
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (A, not B) as 0.",14442
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (not A, not B) as 0.",14443
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (A,B) as 1.",14444
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (not A, B) as 1.",14445
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (A, not B) as 1.",14446
Titanic_decision_tree.png,"Considering that A=True<=>PClass<=2.5 and B=True<=>Parch<=0.5, the Decision Tree presented classifies (not A, not B) as 1.",14447
Titanic_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1249 episodes.,14448
Titanic_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1416 episodes.,14449
Titanic_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1245 episodes.,14450
Titanic_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1392 episodes.,14451
Titanic_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1232 episodes.,14452
Titanic_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 129 estimators.,14453
Titanic_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 105 estimators.,14454
Titanic_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 86 estimators.,14455
Titanic_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 149 estimators.,14456
Titanic_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 59 estimators.,14457
Titanic_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 140 estimators.,14458
Titanic_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 57 estimators.,14459
Titanic_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 112 estimators.,14460
Titanic_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 79 estimators.,14461
Titanic_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 147 estimators.,14462
Titanic_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,14463
Titanic_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,14464
Titanic_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,14465
Titanic_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,14466
Titanic_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,14467
Titanic_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,14468
Titanic_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,14469
Titanic_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,14470
Titanic_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,14471
Titanic_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,14472
Titanic_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,14473
Titanic_decision_tree.png,The recall for the presented tree is higher than its accuracy.,14474
Titanic_decision_tree.png,The precision for the presented tree is higher than its accuracy.,14475
Titanic_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,14476
Titanic_decision_tree.png,The recall for the presented tree is lower than its accuracy.,14477
Titanic_decision_tree.png,The precision for the presented tree is lower than its accuracy.,14478
Titanic_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,14479
Titanic_decision_tree.png,The accuracy for the presented tree is higher than its recall.,14480
Titanic_decision_tree.png,The precision for the presented tree is higher than its recall.,14481
Titanic_decision_tree.png,The specificity for the presented tree is higher than its recall.,14482
Titanic_decision_tree.png,The accuracy for the presented tree is lower than its recall.,14483
Titanic_decision_tree.png,The precision for the presented tree is lower than its recall.,14484
Titanic_decision_tree.png,The specificity for the presented tree is lower than its recall.,14485
Titanic_decision_tree.png,The accuracy for the presented tree is higher than its precision.,14486
Titanic_decision_tree.png,The recall for the presented tree is higher than its precision.,14487
Titanic_decision_tree.png,The specificity for the presented tree is higher than its precision.,14488
Titanic_decision_tree.png,The accuracy for the presented tree is lower than its precision.,14489
Titanic_decision_tree.png,The recall for the presented tree is lower than its precision.,14490
Titanic_decision_tree.png,The specificity for the presented tree is lower than its precision.,14491
Titanic_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,14492
Titanic_decision_tree.png,The recall for the presented tree is higher than its specificity.,14493
Titanic_decision_tree.png,The precision for the presented tree is higher than its specificity.,14494
Titanic_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,14495
Titanic_decision_tree.png,The recall for the presented tree is lower than its specificity.,14496
Titanic_decision_tree.png,The precision for the presented tree is lower than its specificity.,14497
Titanic_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,14498
Titanic_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,14499
Titanic_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,14500
Titanic_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,14501
Titanic_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,14502
Titanic_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,14503
Titanic_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,14504
Titanic_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,14505
Titanic_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,14506
Titanic_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,14507
Titanic_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,14508
Titanic_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,14509
Titanic_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,14510
Titanic_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,14511
Titanic_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,14512
Titanic_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,14513
Titanic_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,14514
Titanic_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,14515
Titanic_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,14516
Titanic_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,14517
Titanic_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,14518
Titanic_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,14519
Titanic_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,14520
Titanic_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,14521
Titanic_decision_tree.png,The number of True Positives reported in the same tree is 35.,14522
Titanic_decision_tree.png,The number of False Positives reported in the same tree is 26.,14523
Titanic_decision_tree.png,The number of True Negatives reported in the same tree is 22.,14524
Titanic_decision_tree.png,The number of False Negatives reported in the same tree is 25.,14525
Titanic_decision_tree.png,The number of True Positives reported in the same tree is 50.,14526
Titanic_decision_tree.png,The number of False Positives reported in the same tree is 15.,14527
Titanic_decision_tree.png,The number of True Negatives reported in the same tree is 30.,14528
Titanic_decision_tree.png,The number of False Negatives reported in the same tree is 37.,14529
Titanic_decision_tree.png,The number of True Positives reported in the same tree is 47.,14530
Titanic_decision_tree.png,The number of False Positives reported in the same tree is 19.,14531
Titanic_decision_tree.png,The number of True Negatives reported in the same tree is 40.,14532
Titanic_decision_tree.png,The number of False Negatives reported in the same tree is 12.,14533
Titanic_decision_tree.png,The number of True Positives reported in the same tree is 11.,14534
Titanic_decision_tree.png,The number of False Positives reported in the same tree is 41.,14535
Titanic_decision_tree.png,The number of True Negatives reported in the same tree is 13.,14536
Titanic_decision_tree.png,The number of False Negatives reported in the same tree is 36.,14537
Titanic_decision_tree.png,The number of True Positives reported in the same tree is 18.,14538
Titanic_decision_tree.png,The number of False Positives reported in the same tree is 16.,14539
Titanic_decision_tree.png,The number of True Negatives reported in the same tree is 28.,14540
Titanic_decision_tree.png,The number of False Negatives reported in the same tree is 44.,14541
Titanic_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,14542
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,14543
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,14544
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,14545
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,14546
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,14547
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,14548
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,14549
Titanic_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,14550
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,14551
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,14552
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,14553
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,14554
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,14555
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,14556
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,14557
Titanic_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,14558
Titanic_decision_tree.png,The accuracy for the presented tree is higher than 83%.,14559
Titanic_decision_tree.png,The recall for the presented tree is higher than 82%.,14560
Titanic_decision_tree.png,The precision for the presented tree is higher than 84%.,14561
Titanic_decision_tree.png,The specificity for the presented tree is higher than 85%.,14562
Titanic_decision_tree.png,The accuracy for the presented tree is lower than 62%.,14563
Titanic_decision_tree.png,The recall for the presented tree is lower than 87%.,14564
Titanic_decision_tree.png,The precision for the presented tree is lower than 65%.,14565
Titanic_decision_tree.png,The specificity for the presented tree is lower than 68%.,14566
Titanic_decision_tree.png,The accuracy for the presented tree is higher than 86%.,14567
Titanic_decision_tree.png,The recall for the presented tree is higher than 61%.,14568
Titanic_decision_tree.png,The precision for the presented tree is higher than 81%.,14569
Titanic_decision_tree.png,The specificity for the presented tree is higher than 67%.,14570
Titanic_decision_tree.png,The accuracy for the presented tree is lower than 69%.,14571
Titanic_decision_tree.png,The recall for the presented tree is lower than 88%.,14572
Titanic_decision_tree.png,The precision for the presented tree is lower than 76%.,14573
Titanic_decision_tree.png,The specificity for the presented tree is lower than 73%.,14574
Titanic_decision_tree.png,The accuracy for the presented tree is higher than 60%.,14575
Titanic_decision_tree.png,The recall for the presented tree is higher than 77%.,14576
Titanic_decision_tree.png,The precision for the presented tree is higher than 72%.,14577
Titanic_decision_tree.png,The specificity for the presented tree is higher than 74%.,14578
Titanic_decision_tree.png,The accuracy for the presented tree is lower than 63%.,14579
Titanic_decision_tree.png,The recall for the presented tree is lower than 71%.,14580
Titanic_decision_tree.png,The precision for the presented tree is lower than 79%.,14581
Titanic_decision_tree.png,The specificity for the presented tree is lower than 75%.,14582
Titanic_decision_tree.png,The accuracy for the presented tree is higher than 70%.,14583
Titanic_decision_tree.png,The recall for the presented tree is higher than 64%.,14584
Titanic_decision_tree.png,The precision for the presented tree is higher than 66%.,14585
Titanic_decision_tree.png,The specificity for the presented tree is higher than 78%.,14586
Titanic_decision_tree.png,The accuracy for the presented tree is lower than 89%.,14587
Titanic_decision_tree.png,The recall for the presented tree is lower than 90%.,14588
Titanic_decision_tree.png,The precision for the presented tree is lower than 80%.,14589
Titanic_decision_tree.png,The specificity for the presented tree is lower than 66%.,14590
Titanic_decision_tree.png,The accuracy for the presented tree is higher than 73%.,14591
Titanic_decision_tree.png,The recall for the presented tree is higher than 79%.,14592
Titanic_decision_tree.png,The precision for the presented tree is higher than 62%.,14593
Titanic_decision_tree.png,The specificity for the presented tree is higher than 82%.,14594
Titanic_decision_tree.png,The accuracy for the presented tree is lower than 78%.,14595
Titanic_decision_tree.png,The recall for the presented tree is lower than 64%.,14596
Titanic_decision_tree.png,The precision for the presented tree is lower than 66%.,14597
Titanic_decision_tree.png,The specificity for the presented tree is lower than 89%.,14598
Titanic_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",14599
Titanic_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",14600
Titanic_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",14601
Titanic_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",14602
Titanic_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",14603
Titanic_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",14604
Titanic_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,14605
Titanic_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,14606
Titanic_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,14607
Titanic_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,14608
Titanic_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,14609
Titanic_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,14610
Titanic_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,14611
Titanic_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,14612
Titanic_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,14613
Titanic_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,14614
Titanic_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,14615
Titanic_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,14616
Titanic_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,14617
Titanic_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,14618
Titanic_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,14619
Titanic_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,14620
Titanic_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,14621
Titanic_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,14622
Titanic_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,14623
Titanic_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,14624
Titanic_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,14625
Titanic_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,14626
Titanic_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,14627
Titanic_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,14628
Titanic_overfitting_knn.png,KNN is in overfitting for k less than 2.,14629
Titanic_overfitting_knn.png,KNN is in overfitting for k larger than 2.,14630
Titanic_overfitting_knn.png,KNN is in overfitting for k less than 3.,14631
Titanic_overfitting_knn.png,KNN is in overfitting for k larger than 3.,14632
Titanic_overfitting_knn.png,KNN is in overfitting for k less than 4.,14633
Titanic_overfitting_knn.png,KNN is in overfitting for k larger than 4.,14634
Titanic_overfitting_knn.png,KNN is in overfitting for k less than 5.,14635
Titanic_overfitting_knn.png,KNN is in overfitting for k larger than 5.,14636
Titanic_overfitting_knn.png,KNN is in overfitting for k less than 6.,14637
Titanic_overfitting_knn.png,KNN is in overfitting for k larger than 6.,14638
Titanic_overfitting_knn.png,KNN is in overfitting for k less than 7.,14639
Titanic_overfitting_knn.png,KNN is in overfitting for k larger than 7.,14640
Titanic_overfitting_knn.png,KNN is in overfitting for k less than 8.,14641
Titanic_overfitting_knn.png,KNN is in overfitting for k larger than 8.,14642
Titanic_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",14643
Titanic_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",14644
Titanic_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",14645
Titanic_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",14646
Titanic_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",14647
Titanic_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",14648
Titanic_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",14649
Titanic_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",14650
Titanic_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",14651
Titanic_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",14652
Titanic_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",14653
Titanic_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",14654
Titanic_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",14655
Titanic_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",14656
Titanic_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,14657
Titanic_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,14658
Titanic_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,14659
Titanic_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,14660
Titanic_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,14661
Titanic_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,14662
Titanic_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,14663
Titanic_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,14664
Titanic_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,14665
Titanic_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,14666
Titanic_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,14667
Titanic_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,14668
Titanic_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,14669
Titanic_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,14670
Titanic_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,14671
Titanic_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,14672
Titanic_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,14673
Titanic_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,14674
Titanic_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,14675
Titanic_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,14676
Titanic_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,14677
Titanic_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,14678
Titanic_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,14679
Titanic_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,14680
Titanic_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,14681
Titanic_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,14682
Titanic_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,14683
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Pclass.,14684
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SibSp previously than variable Pclass.,14685
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Parch previously than variable Pclass.,14686
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Fare previously than variable Pclass.,14687
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pclass previously than variable Age.,14688
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SibSp previously than variable Age.,14689
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Parch previously than variable Age.,14690
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Fare previously than variable Age.,14691
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pclass previously than variable SibSp.,14692
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable SibSp.,14693
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Parch previously than variable SibSp.,14694
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Fare previously than variable SibSp.,14695
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pclass previously than variable Parch.,14696
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Parch.,14697
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SibSp previously than variable Parch.,14698
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Fare previously than variable Parch.,14699
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pclass previously than variable Fare.,14700
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Fare.,14701
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SibSp previously than variable Fare.,14702
Titanic_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Parch previously than variable Fare.,14703
Titanic_pca.png,The first 2 principal components are enough for explaining half the data variance.,14704
Titanic_pca.png,The first 3 principal components are enough for explaining half the data variance.,14705
Titanic_pca.png,The first 4 principal components are enough for explaining half the data variance.,14706
Titanic_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,14707
Titanic_correlation_heatmap.png,Removing variable Pclass might improve the training of decision trees .,14708
Titanic_correlation_heatmap.png,Removing variable Age might improve the training of decision trees .,14709
Titanic_correlation_heatmap.png,Removing variable SibSp might improve the training of decision trees .,14710
Titanic_correlation_heatmap.png,Removing variable Parch might improve the training of decision trees .,14711
Titanic_correlation_heatmap.png,Removing variable Fare might improve the training of decision trees .,14712
Titanic_histograms.png,"Not knowing the semantics of Pclass variable, dummification could have been a more adequate codification.",14713
Titanic_histograms.png,"Not knowing the semantics of Sex variable, dummification could have been a more adequate codification.",14714
Titanic_histograms.png,"Not knowing the semantics of Age variable, dummification could have been a more adequate codification.",14715
Titanic_histograms.png,"Not knowing the semantics of SibSp variable, dummification could have been a more adequate codification.",14716
Titanic_histograms.png,"Not knowing the semantics of Parch variable, dummification could have been a more adequate codification.",14717
Titanic_histograms.png,"Not knowing the semantics of Fare variable, dummification could have been a more adequate codification.",14718
Titanic_histograms.png,"Not knowing the semantics of Embarked variable, dummification could have been a more adequate codification.",14719
Titanic_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,14720
Titanic_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",14721
Titanic_histograms.png,It is better to drop the variable Pclass than removing all records with missing values.,14722
Titanic_histograms.png,It is better to drop the variable Sex than removing all records with missing values.,14723
Titanic_histograms.png,It is better to drop the variable Age than removing all records with missing values.,14724
Titanic_histograms.png,It is better to drop the variable SibSp than removing all records with missing values.,14725
Titanic_histograms.png,It is better to drop the variable Parch than removing all records with missing values.,14726
Titanic_histograms.png,It is better to drop the variable Fare than removing all records with missing values.,14727
Titanic_histograms.png,It is better to drop the variable Embarked than removing all records with missing values.,14728
Titanic_histograms.png,"Given the usual semantics of Pclass variable, dummification would have been a better codification.",14729
Titanic_histograms.png,"Given the usual semantics of Sex variable, dummification would have been a better codification.",14730
Titanic_histograms.png,"Given the usual semantics of Age variable, dummification would have been a better codification.",14731
Titanic_histograms.png,"Given the usual semantics of SibSp variable, dummification would have been a better codification.",14732
Titanic_histograms.png,"Given the usual semantics of Parch variable, dummification would have been a better codification.",14733
Titanic_histograms.png,"Given the usual semantics of Fare variable, dummification would have been a better codification.",14734
Titanic_histograms.png,"Given the usual semantics of Embarked variable, dummification would have been a better codification.",14735
Titanic_histograms.png,"Feature generation based on the use of variable Sex wouldn’t be useful, but the use of Pclass seems to be promising.",14736
Titanic_histograms.png,"Feature generation based on the use of variable Age wouldn’t be useful, but the use of Pclass seems to be promising.",14737
Titanic_histograms.png,"Feature generation based on the use of variable SibSp wouldn’t be useful, but the use of Pclass seems to be promising.",14738
Titanic_histograms.png,"Feature generation based on the use of variable Parch wouldn’t be useful, but the use of Pclass seems to be promising.",14739
Titanic_histograms.png,"Feature generation based on the use of variable Fare wouldn’t be useful, but the use of Pclass seems to be promising.",14740
Titanic_histograms.png,"Feature generation based on the use of variable Embarked wouldn’t be useful, but the use of Pclass seems to be promising.",14741
Titanic_histograms.png,"Feature generation based on the use of variable Pclass wouldn’t be useful, but the use of Sex seems to be promising.",14742
Titanic_histograms.png,"Feature generation based on the use of variable Age wouldn’t be useful, but the use of Sex seems to be promising.",14743
Titanic_histograms.png,"Feature generation based on the use of variable SibSp wouldn’t be useful, but the use of Sex seems to be promising.",14744
Titanic_histograms.png,"Feature generation based on the use of variable Parch wouldn’t be useful, but the use of Sex seems to be promising.",14745
Titanic_histograms.png,"Feature generation based on the use of variable Fare wouldn’t be useful, but the use of Sex seems to be promising.",14746
Titanic_histograms.png,"Feature generation based on the use of variable Embarked wouldn’t be useful, but the use of Sex seems to be promising.",14747
Titanic_histograms.png,"Feature generation based on the use of variable Pclass wouldn’t be useful, but the use of Age seems to be promising.",14748
Titanic_histograms.png,"Feature generation based on the use of variable Sex wouldn’t be useful, but the use of Age seems to be promising.",14749
Titanic_histograms.png,"Feature generation based on the use of variable SibSp wouldn’t be useful, but the use of Age seems to be promising.",14750
Titanic_histograms.png,"Feature generation based on the use of variable Parch wouldn’t be useful, but the use of Age seems to be promising.",14751
Titanic_histograms.png,"Feature generation based on the use of variable Fare wouldn’t be useful, but the use of Age seems to be promising.",14752
Titanic_histograms.png,"Feature generation based on the use of variable Embarked wouldn’t be useful, but the use of Age seems to be promising.",14753
Titanic_histograms.png,"Feature generation based on the use of variable Pclass wouldn’t be useful, but the use of SibSp seems to be promising.",14754
Titanic_histograms.png,"Feature generation based on the use of variable Sex wouldn’t be useful, but the use of SibSp seems to be promising.",14755
Titanic_histograms.png,"Feature generation based on the use of variable Age wouldn’t be useful, but the use of SibSp seems to be promising.",14756
Titanic_histograms.png,"Feature generation based on the use of variable Parch wouldn’t be useful, but the use of SibSp seems to be promising.",14757
Titanic_histograms.png,"Feature generation based on the use of variable Fare wouldn’t be useful, but the use of SibSp seems to be promising.",14758
Titanic_histograms.png,"Feature generation based on the use of variable Embarked wouldn’t be useful, but the use of SibSp seems to be promising.",14759
Titanic_histograms.png,"Feature generation based on the use of variable Pclass wouldn’t be useful, but the use of Parch seems to be promising.",14760
Titanic_histograms.png,"Feature generation based on the use of variable Sex wouldn’t be useful, but the use of Parch seems to be promising.",14761
Titanic_histograms.png,"Feature generation based on the use of variable Age wouldn’t be useful, but the use of Parch seems to be promising.",14762
Titanic_histograms.png,"Feature generation based on the use of variable SibSp wouldn’t be useful, but the use of Parch seems to be promising.",14763
Titanic_histograms.png,"Feature generation based on the use of variable Fare wouldn’t be useful, but the use of Parch seems to be promising.",14764
Titanic_histograms.png,"Feature generation based on the use of variable Embarked wouldn’t be useful, but the use of Parch seems to be promising.",14765
Titanic_histograms.png,"Feature generation based on the use of variable Pclass wouldn’t be useful, but the use of Fare seems to be promising.",14766
Titanic_histograms.png,"Feature generation based on the use of variable Sex wouldn’t be useful, but the use of Fare seems to be promising.",14767
Titanic_histograms.png,"Feature generation based on the use of variable Age wouldn’t be useful, but the use of Fare seems to be promising.",14768
Titanic_histograms.png,"Feature generation based on the use of variable SibSp wouldn’t be useful, but the use of Fare seems to be promising.",14769
Titanic_histograms.png,"Feature generation based on the use of variable Parch wouldn’t be useful, but the use of Fare seems to be promising.",14770
Titanic_histograms.png,"Feature generation based on the use of variable Embarked wouldn’t be useful, but the use of Fare seems to be promising.",14771
Titanic_histograms.png,"Feature generation based on the use of variable Pclass wouldn’t be useful, but the use of Embarked seems to be promising.",14772
Titanic_histograms.png,"Feature generation based on the use of variable Sex wouldn’t be useful, but the use of Embarked seems to be promising.",14773
Titanic_histograms.png,"Feature generation based on the use of variable Age wouldn’t be useful, but the use of Embarked seems to be promising.",14774
Titanic_histograms.png,"Feature generation based on the use of variable SibSp wouldn’t be useful, but the use of Embarked seems to be promising.",14775
Titanic_histograms.png,"Feature generation based on the use of variable Parch wouldn’t be useful, but the use of Embarked seems to be promising.",14776
Titanic_histograms.png,"Feature generation based on the use of variable Fare wouldn’t be useful, but the use of Embarked seems to be promising.",14777
Titanic_histograms.png,Feature generation based on both variables Sex and Pclass seems to be promising.,14778
Titanic_histograms.png,Feature generation based on both variables Age and Pclass seems to be promising.,14779
Titanic_histograms.png,Feature generation based on both variables SibSp and Pclass seems to be promising.,14780
Titanic_histograms.png,Feature generation based on both variables Parch and Pclass seems to be promising.,14781
Titanic_histograms.png,Feature generation based on both variables Fare and Pclass seems to be promising.,14782
Titanic_histograms.png,Feature generation based on both variables Embarked and Pclass seems to be promising.,14783
Titanic_histograms.png,Feature generation based on both variables Pclass and Sex seems to be promising.,14784
Titanic_histograms.png,Feature generation based on both variables Age and Sex seems to be promising.,14785
Titanic_histograms.png,Feature generation based on both variables SibSp and Sex seems to be promising.,14786
Titanic_histograms.png,Feature generation based on both variables Parch and Sex seems to be promising.,14787
Titanic_histograms.png,Feature generation based on both variables Fare and Sex seems to be promising.,14788
Titanic_histograms.png,Feature generation based on both variables Embarked and Sex seems to be promising.,14789
Titanic_histograms.png,Feature generation based on both variables Pclass and Age seems to be promising.,14790
Titanic_histograms.png,Feature generation based on both variables Sex and Age seems to be promising.,14791
Titanic_histograms.png,Feature generation based on both variables SibSp and Age seems to be promising.,14792
Titanic_histograms.png,Feature generation based on both variables Parch and Age seems to be promising.,14793
Titanic_histograms.png,Feature generation based on both variables Fare and Age seems to be promising.,14794
Titanic_histograms.png,Feature generation based on both variables Embarked and Age seems to be promising.,14795
Titanic_histograms.png,Feature generation based on both variables Pclass and SibSp seems to be promising.,14796
Titanic_histograms.png,Feature generation based on both variables Sex and SibSp seems to be promising.,14797
Titanic_histograms.png,Feature generation based on both variables Age and SibSp seems to be promising.,14798
Titanic_histograms.png,Feature generation based on both variables Parch and SibSp seems to be promising.,14799
Titanic_histograms.png,Feature generation based on both variables Fare and SibSp seems to be promising.,14800
Titanic_histograms.png,Feature generation based on both variables Embarked and SibSp seems to be promising.,14801
Titanic_histograms.png,Feature generation based on both variables Pclass and Parch seems to be promising.,14802
Titanic_histograms.png,Feature generation based on both variables Sex and Parch seems to be promising.,14803
Titanic_histograms.png,Feature generation based on both variables Age and Parch seems to be promising.,14804
Titanic_histograms.png,Feature generation based on both variables SibSp and Parch seems to be promising.,14805
Titanic_histograms.png,Feature generation based on both variables Fare and Parch seems to be promising.,14806
Titanic_histograms.png,Feature generation based on both variables Embarked and Parch seems to be promising.,14807
Titanic_histograms.png,Feature generation based on both variables Pclass and Fare seems to be promising.,14808
Titanic_histograms.png,Feature generation based on both variables Sex and Fare seems to be promising.,14809
Titanic_histograms.png,Feature generation based on both variables Age and Fare seems to be promising.,14810
Titanic_histograms.png,Feature generation based on both variables SibSp and Fare seems to be promising.,14811
Titanic_histograms.png,Feature generation based on both variables Parch and Fare seems to be promising.,14812
Titanic_histograms.png,Feature generation based on both variables Embarked and Fare seems to be promising.,14813
Titanic_histograms.png,Feature generation based on both variables Pclass and Embarked seems to be promising.,14814
Titanic_histograms.png,Feature generation based on both variables Sex and Embarked seems to be promising.,14815
Titanic_histograms.png,Feature generation based on both variables Age and Embarked seems to be promising.,14816
Titanic_histograms.png,Feature generation based on both variables SibSp and Embarked seems to be promising.,14817
Titanic_histograms.png,Feature generation based on both variables Parch and Embarked seems to be promising.,14818
Titanic_histograms.png,Feature generation based on both variables Fare and Embarked seems to be promising.,14819
Titanic_mv.png,There is no reason to believe that discarding records showing missing values is safer than discarding the corresponding variables in this case.,14820
Titanic_mv.png,Dropping all rows with missing values can lead to a dataset with less than 25% of the original data.,14821
Titanic_mv.png,Dropping all rows with missing values can lead to a dataset with less than 30% of the original data.,14822
Titanic_mv.png,Dropping all rows with missing values can lead to a dataset with less than 40% of the original data.,14823
Titanic_mv.png,Dropping all records with missing values would be better than to drop the variables with missing values.,14824
Titanic_mv.png,Discarding variables Sex and Pclass would be better than discarding all the records with missing values for those variables.,14825
Titanic_mv.png,Discarding variables Age and Pclass would be better than discarding all the records with missing values for those variables.,14826
Titanic_mv.png,Discarding variables SibSp and Pclass would be better than discarding all the records with missing values for those variables.,14827
Titanic_mv.png,Discarding variables Parch and Pclass would be better than discarding all the records with missing values for those variables.,14828
Titanic_mv.png,Discarding variables Fare and Pclass would be better than discarding all the records with missing values for those variables.,14829
Titanic_mv.png,Discarding variables Embarked and Pclass would be better than discarding all the records with missing values for those variables.,14830
Titanic_mv.png,Discarding variables Pclass and Sex would be better than discarding all the records with missing values for those variables.,14831
Titanic_mv.png,Discarding variables Age and Sex would be better than discarding all the records with missing values for those variables.,14832
Titanic_mv.png,Discarding variables SibSp and Sex would be better than discarding all the records with missing values for those variables.,14833
Titanic_mv.png,Discarding variables Parch and Sex would be better than discarding all the records with missing values for those variables.,14834
Titanic_mv.png,Discarding variables Fare and Sex would be better than discarding all the records with missing values for those variables.,14835
Titanic_mv.png,Discarding variables Embarked and Sex would be better than discarding all the records with missing values for those variables.,14836
Titanic_mv.png,Discarding variables Pclass and Age would be better than discarding all the records with missing values for those variables.,14837
Titanic_mv.png,Discarding variables Sex and Age would be better than discarding all the records with missing values for those variables.,14838
Titanic_mv.png,Discarding variables SibSp and Age would be better than discarding all the records with missing values for those variables.,14839
Titanic_mv.png,Discarding variables Parch and Age would be better than discarding all the records with missing values for those variables.,14840
Titanic_mv.png,Discarding variables Fare and Age would be better than discarding all the records with missing values for those variables.,14841
Titanic_mv.png,Discarding variables Embarked and Age would be better than discarding all the records with missing values for those variables.,14842
Titanic_mv.png,Discarding variables Pclass and SibSp would be better than discarding all the records with missing values for those variables.,14843
Titanic_mv.png,Discarding variables Sex and SibSp would be better than discarding all the records with missing values for those variables.,14844
Titanic_mv.png,Discarding variables Age and SibSp would be better than discarding all the records with missing values for those variables.,14845
Titanic_mv.png,Discarding variables Parch and SibSp would be better than discarding all the records with missing values for those variables.,14846
Titanic_mv.png,Discarding variables Fare and SibSp would be better than discarding all the records with missing values for those variables.,14847
Titanic_mv.png,Discarding variables Embarked and SibSp would be better than discarding all the records with missing values for those variables.,14848
Titanic_mv.png,Discarding variables Pclass and Parch would be better than discarding all the records with missing values for those variables.,14849
Titanic_mv.png,Discarding variables Sex and Parch would be better than discarding all the records with missing values for those variables.,14850
Titanic_mv.png,Discarding variables Age and Parch would be better than discarding all the records with missing values for those variables.,14851
Titanic_mv.png,Discarding variables SibSp and Parch would be better than discarding all the records with missing values for those variables.,14852
Titanic_mv.png,Discarding variables Fare and Parch would be better than discarding all the records with missing values for those variables.,14853
Titanic_mv.png,Discarding variables Embarked and Parch would be better than discarding all the records with missing values for those variables.,14854
Titanic_mv.png,Discarding variables Pclass and Fare would be better than discarding all the records with missing values for those variables.,14855
Titanic_mv.png,Discarding variables Sex and Fare would be better than discarding all the records with missing values for those variables.,14856
Titanic_mv.png,Discarding variables Age and Fare would be better than discarding all the records with missing values for those variables.,14857
Titanic_mv.png,Discarding variables SibSp and Fare would be better than discarding all the records with missing values for those variables.,14858
Titanic_mv.png,Discarding variables Parch and Fare would be better than discarding all the records with missing values for those variables.,14859
Titanic_mv.png,Discarding variables Embarked and Fare would be better than discarding all the records with missing values for those variables.,14860
Titanic_mv.png,Discarding variables Pclass and Embarked would be better than discarding all the records with missing values for those variables.,14861
Titanic_mv.png,Discarding variables Sex and Embarked would be better than discarding all the records with missing values for those variables.,14862
Titanic_mv.png,Discarding variables Age and Embarked would be better than discarding all the records with missing values for those variables.,14863
Titanic_mv.png,Discarding variables SibSp and Embarked would be better than discarding all the records with missing values for those variables.,14864
Titanic_mv.png,Discarding variables Parch and Embarked would be better than discarding all the records with missing values for those variables.,14865
Titanic_mv.png,Discarding variables Fare and Embarked would be better than discarding all the records with missing values for those variables.,14866
Titanic_histograms.png,The variable Pclass can be coded as ordinal without losing information.,14867
Titanic_histograms.png,The variable Sex can be coded as ordinal without losing information.,14868
Titanic_histograms.png,The variable Age can be coded as ordinal without losing information.,14869
Titanic_histograms.png,The variable SibSp can be coded as ordinal without losing information.,14870
Titanic_histograms.png,The variable Parch can be coded as ordinal without losing information.,14871
Titanic_histograms.png,The variable Fare can be coded as ordinal without losing information.,14872
Titanic_histograms.png,The variable Embarked can be coded as ordinal without losing information.,14873
Titanic_histograms.png,"Considering the common semantics for Pclass variable, dummification would be the most adequate encoding.",14874
Titanic_histograms.png,"Considering the common semantics for Sex variable, dummification would be the most adequate encoding.",14875
Titanic_histograms.png,"Considering the common semantics for Age variable, dummification would be the most adequate encoding.",14876
Titanic_histograms.png,"Considering the common semantics for SibSp variable, dummification would be the most adequate encoding.",14877
Titanic_histograms.png,"Considering the common semantics for Parch variable, dummification would be the most adequate encoding.",14878
Titanic_histograms.png,"Considering the common semantics for Fare variable, dummification would be the most adequate encoding.",14879
Titanic_histograms.png,"Considering the common semantics for Embarked variable, dummification would be the most adequate encoding.",14880
Titanic_histograms.png,"Considering the common semantics for Sex and Pclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14881
Titanic_histograms.png,"Considering the common semantics for Age and Pclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14882
Titanic_histograms.png,"Considering the common semantics for SibSp and Pclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14883
Titanic_histograms.png,"Considering the common semantics for Parch and Pclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14884
Titanic_histograms.png,"Considering the common semantics for Fare and Pclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14885
Titanic_histograms.png,"Considering the common semantics for Embarked and Pclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14886
Titanic_histograms.png,"Considering the common semantics for Pclass and Sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14887
Titanic_histograms.png,"Considering the common semantics for Age and Sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14888
Titanic_histograms.png,"Considering the common semantics for SibSp and Sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14889
Titanic_histograms.png,"Considering the common semantics for Parch and Sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14890
Titanic_histograms.png,"Considering the common semantics for Fare and Sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14891
Titanic_histograms.png,"Considering the common semantics for Embarked and Sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14892
Titanic_histograms.png,"Considering the common semantics for Pclass and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14893
Titanic_histograms.png,"Considering the common semantics for Sex and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14894
Titanic_histograms.png,"Considering the common semantics for SibSp and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14895
Titanic_histograms.png,"Considering the common semantics for Parch and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14896
Titanic_histograms.png,"Considering the common semantics for Fare and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14897
Titanic_histograms.png,"Considering the common semantics for Embarked and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14898
Titanic_histograms.png,"Considering the common semantics for Pclass and SibSp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14899
Titanic_histograms.png,"Considering the common semantics for Sex and SibSp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14900
Titanic_histograms.png,"Considering the common semantics for Age and SibSp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14901
Titanic_histograms.png,"Considering the common semantics for Parch and SibSp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14902
Titanic_histograms.png,"Considering the common semantics for Fare and SibSp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14903
Titanic_histograms.png,"Considering the common semantics for Embarked and SibSp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14904
Titanic_histograms.png,"Considering the common semantics for Pclass and Parch variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14905
Titanic_histograms.png,"Considering the common semantics for Sex and Parch variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14906
Titanic_histograms.png,"Considering the common semantics for Age and Parch variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14907
Titanic_histograms.png,"Considering the common semantics for SibSp and Parch variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14908
Titanic_histograms.png,"Considering the common semantics for Fare and Parch variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14909
Titanic_histograms.png,"Considering the common semantics for Embarked and Parch variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14910
Titanic_histograms.png,"Considering the common semantics for Pclass and Fare variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14911
Titanic_histograms.png,"Considering the common semantics for Sex and Fare variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14912
Titanic_histograms.png,"Considering the common semantics for Age and Fare variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14913
Titanic_histograms.png,"Considering the common semantics for SibSp and Fare variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14914
Titanic_histograms.png,"Considering the common semantics for Parch and Fare variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14915
Titanic_histograms.png,"Considering the common semantics for Embarked and Fare variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14916
Titanic_histograms.png,"Considering the common semantics for Pclass and Embarked variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14917
Titanic_histograms.png,"Considering the common semantics for Sex and Embarked variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14918
Titanic_histograms.png,"Considering the common semantics for Age and Embarked variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14919
Titanic_histograms.png,"Considering the common semantics for SibSp and Embarked variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14920
Titanic_histograms.png,"Considering the common semantics for Parch and Embarked variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14921
Titanic_histograms.png,"Considering the common semantics for Fare and Embarked variables, dummification if applied would increase the risk of facing the curse of dimensionality.",14922
Titanic_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,14923
Titanic_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,14924
Titanic_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,14925
Titanic_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",14926
Titanic_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",14927
Titanic_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",14928
Titanic_correlation_heatmap.png,Variables Age and Pclass seem to be useful for classification tasks.,14929
Titanic_correlation_heatmap.png,Variables SibSp and Pclass seem to be useful for classification tasks.,14930
Titanic_correlation_heatmap.png,Variables Parch and Pclass seem to be useful for classification tasks.,14931
Titanic_correlation_heatmap.png,Variables Fare and Pclass seem to be useful for classification tasks.,14932
Titanic_correlation_heatmap.png,Variables Pclass and Age seem to be useful for classification tasks.,14933
Titanic_correlation_heatmap.png,Variables SibSp and Age seem to be useful for classification tasks.,14934
Titanic_correlation_heatmap.png,Variables Parch and Age seem to be useful for classification tasks.,14935
Titanic_correlation_heatmap.png,Variables Fare and Age seem to be useful for classification tasks.,14936
Titanic_correlation_heatmap.png,Variables Pclass and SibSp seem to be useful for classification tasks.,14937
Titanic_correlation_heatmap.png,Variables Age and SibSp seem to be useful for classification tasks.,14938
Titanic_correlation_heatmap.png,Variables Parch and SibSp seem to be useful for classification tasks.,14939
Titanic_correlation_heatmap.png,Variables Fare and SibSp seem to be useful for classification tasks.,14940
Titanic_correlation_heatmap.png,Variables Pclass and Parch seem to be useful for classification tasks.,14941
Titanic_correlation_heatmap.png,Variables Age and Parch seem to be useful for classification tasks.,14942
Titanic_correlation_heatmap.png,Variables SibSp and Parch seem to be useful for classification tasks.,14943
Titanic_correlation_heatmap.png,Variables Fare and Parch seem to be useful for classification tasks.,14944
Titanic_correlation_heatmap.png,Variables Pclass and Fare seem to be useful for classification tasks.,14945
Titanic_correlation_heatmap.png,Variables Age and Fare seem to be useful for classification tasks.,14946
Titanic_correlation_heatmap.png,Variables SibSp and Fare seem to be useful for classification tasks.,14947
Titanic_correlation_heatmap.png,Variables Parch and Fare seem to be useful for classification tasks.,14948
Titanic_correlation_heatmap.png,Variable Pclass seems to be relevant for the majority of mining tasks.,14949
Titanic_correlation_heatmap.png,Variable Age seems to be relevant for the majority of mining tasks.,14950
Titanic_correlation_heatmap.png,Variable SibSp seems to be relevant for the majority of mining tasks.,14951
Titanic_correlation_heatmap.png,Variable Parch seems to be relevant for the majority of mining tasks.,14952
Titanic_correlation_heatmap.png,Variable Fare seems to be relevant for the majority of mining tasks.,14953
Titanic_decision_tree.png,Variable Pclass is one of the most relevant variables.,14954
Titanic_decision_tree.png,Variable Age is one of the most relevant variables.,14955
Titanic_decision_tree.png,Variable SibSp is one of the most relevant variables.,14956
Titanic_decision_tree.png,Variable Parch is one of the most relevant variables.,14957
Titanic_decision_tree.png,Variable Fare is one of the most relevant variables.,14958
Titanic_decision_tree.png,It is possible to state that Pclass is the first most discriminative variable regarding the class.,14959
Titanic_decision_tree.png,It is possible to state that Age is the first most discriminative variable regarding the class.,14960
Titanic_decision_tree.png,It is possible to state that SibSp is the first most discriminative variable regarding the class.,14961
Titanic_decision_tree.png,It is possible to state that Parch is the first most discriminative variable regarding the class.,14962
Titanic_decision_tree.png,It is possible to state that Fare is the first most discriminative variable regarding the class.,14963
Titanic_decision_tree.png,It is possible to state that Pclass is the second most discriminative variable regarding the class.,14964
Titanic_decision_tree.png,It is possible to state that Age is the second most discriminative variable regarding the class.,14965
Titanic_decision_tree.png,It is possible to state that SibSp is the second most discriminative variable regarding the class.,14966
Titanic_decision_tree.png,It is possible to state that Parch is the second most discriminative variable regarding the class.,14967
Titanic_decision_tree.png,It is possible to state that Fare is the second most discriminative variable regarding the class.,14968
Titanic_decision_tree.png,"The variable Pclass discriminates between the target values, as shown in the decision tree.",14969
Titanic_decision_tree.png,"The variable Age discriminates between the target values, as shown in the decision tree.",14970
Titanic_decision_tree.png,"The variable SibSp discriminates between the target values, as shown in the decision tree.",14971
Titanic_decision_tree.png,"The variable Parch discriminates between the target values, as shown in the decision tree.",14972
Titanic_decision_tree.png,"The variable Fare discriminates between the target values, as shown in the decision tree.",14973
Titanic_decision_tree.png,The variable Pclass seems to be one of the two most relevant features.,14974
Titanic_decision_tree.png,The variable Age seems to be one of the two most relevant features.,14975
Titanic_decision_tree.png,The variable SibSp seems to be one of the two most relevant features.,14976
Titanic_decision_tree.png,The variable Parch seems to be one of the two most relevant features.,14977
Titanic_decision_tree.png,The variable Fare seems to be one of the two most relevant features.,14978
Titanic_decision_tree.png,The variable Pclass seems to be one of the three most relevant features.,14979
Titanic_decision_tree.png,The variable Age seems to be one of the three most relevant features.,14980
Titanic_decision_tree.png,The variable SibSp seems to be one of the three most relevant features.,14981
Titanic_decision_tree.png,The variable Parch seems to be one of the three most relevant features.,14982
Titanic_decision_tree.png,The variable Fare seems to be one of the three most relevant features.,14983
Titanic_decision_tree.png,The variable Pclass seems to be one of the four most relevant features.,14984
Titanic_decision_tree.png,The variable Age seems to be one of the four most relevant features.,14985
Titanic_decision_tree.png,The variable SibSp seems to be one of the four most relevant features.,14986
Titanic_decision_tree.png,The variable Parch seems to be one of the four most relevant features.,14987
Titanic_decision_tree.png,The variable Fare seems to be one of the four most relevant features.,14988
Titanic_decision_tree.png,The variable Pclass seems to be one of the five most relevant features.,14989
Titanic_decision_tree.png,The variable Age seems to be one of the five most relevant features.,14990
Titanic_decision_tree.png,The variable SibSp seems to be one of the five most relevant features.,14991
Titanic_decision_tree.png,The variable Parch seems to be one of the five most relevant features.,14992
Titanic_decision_tree.png,The variable Fare seems to be one of the five most relevant features.,14993
Titanic_decision_tree.png,It is clear that variable Pclass is one of the two most relevant features.,14994
Titanic_decision_tree.png,It is clear that variable Age is one of the two most relevant features.,14995
Titanic_decision_tree.png,It is clear that variable SibSp is one of the two most relevant features.,14996
Titanic_decision_tree.png,It is clear that variable Parch is one of the two most relevant features.,14997
Titanic_decision_tree.png,It is clear that variable Fare is one of the two most relevant features.,14998
Titanic_decision_tree.png,It is clear that variable Pclass is one of the three most relevant features.,14999
Titanic_decision_tree.png,It is clear that variable Age is one of the three most relevant features.,15000
Titanic_decision_tree.png,It is clear that variable SibSp is one of the three most relevant features.,15001
Titanic_decision_tree.png,It is clear that variable Parch is one of the three most relevant features.,15002
Titanic_decision_tree.png,It is clear that variable Fare is one of the three most relevant features.,15003
Titanic_decision_tree.png,It is clear that variable Pclass is one of the four most relevant features.,15004
Titanic_decision_tree.png,It is clear that variable Age is one of the four most relevant features.,15005
Titanic_decision_tree.png,It is clear that variable SibSp is one of the four most relevant features.,15006
Titanic_decision_tree.png,It is clear that variable Parch is one of the four most relevant features.,15007
Titanic_decision_tree.png,It is clear that variable Fare is one of the four most relevant features.,15008
Titanic_decision_tree.png,It is clear that variable Pclass is one of the five most relevant features.,15009
Titanic_decision_tree.png,It is clear that variable Age is one of the five most relevant features.,15010
Titanic_decision_tree.png,It is clear that variable SibSp is one of the five most relevant features.,15011
Titanic_decision_tree.png,It is clear that variable Parch is one of the five most relevant features.,15012
Titanic_decision_tree.png,It is clear that variable Fare is one of the five most relevant features.,15013
Titanic_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",15014
Titanic_correlation_heatmap.png,Variables Age and Pclass are redundant.,15015
Titanic_correlation_heatmap.png,Variables SibSp and Pclass are redundant.,15016
Titanic_correlation_heatmap.png,Variables Parch and Pclass are redundant.,15017
Titanic_correlation_heatmap.png,Variables Fare and Pclass are redundant.,15018
Titanic_correlation_heatmap.png,Variables Pclass and Age are redundant.,15019
Titanic_correlation_heatmap.png,Variables SibSp and Age are redundant.,15020
Titanic_correlation_heatmap.png,Variables Parch and Age are redundant.,15021
Titanic_correlation_heatmap.png,Variables Fare and Age are redundant.,15022
Titanic_correlation_heatmap.png,Variables Pclass and SibSp are redundant.,15023
Titanic_correlation_heatmap.png,Variables Age and SibSp are redundant.,15024
Titanic_correlation_heatmap.png,Variables Parch and SibSp are redundant.,15025
Titanic_correlation_heatmap.png,Variables Fare and SibSp are redundant.,15026
Titanic_correlation_heatmap.png,Variables Pclass and Parch are redundant.,15027
Titanic_correlation_heatmap.png,Variables Age and Parch are redundant.,15028
Titanic_correlation_heatmap.png,Variables SibSp and Parch are redundant.,15029
Titanic_correlation_heatmap.png,Variables Fare and Parch are redundant.,15030
Titanic_correlation_heatmap.png,Variables Pclass and Fare are redundant.,15031
Titanic_correlation_heatmap.png,Variables Age and Fare are redundant.,15032
Titanic_correlation_heatmap.png,Variables SibSp and Fare are redundant.,15033
Titanic_correlation_heatmap.png,Variables Parch and Fare are redundant.,15034
Titanic_correlation_heatmap.png,"Variables Fare and Embarked are redundant, but we can’t say the same for the pair Sex and SibSp.",15035
Titanic_correlation_heatmap.png,"Variables SibSp and Parch are redundant, but we can’t say the same for the pair Sex and Fare.",15036
Titanic_correlation_heatmap.png,"Variables Parch and Fare are redundant, but we can’t say the same for the pair Sex and Embarked.",15037
Titanic_correlation_heatmap.png,"Variables SibSp and Embarked are redundant, but we can’t say the same for the pair Pclass and Fare.",15038
Titanic_correlation_heatmap.png,"Variables SibSp and Parch are redundant, but we can’t say the same for the pair Fare and Embarked.",15039
Titanic_correlation_heatmap.png,"Variables Pclass and Embarked are redundant, but we can’t say the same for the pair Sex and Age.",15040
Titanic_correlation_heatmap.png,"Variables Fare and Embarked are redundant, but we can’t say the same for the pair Sex and Parch.",15041
Titanic_correlation_heatmap.png,"Variables Pclass and SibSp are redundant, but we can’t say the same for the pair Sex and Parch.",15042
Titanic_correlation_heatmap.png,"Variables SibSp and Parch are redundant, but we can’t say the same for the pair Age and Embarked.",15043
Titanic_correlation_heatmap.png,"Variables Sex and Age are redundant, but we can’t say the same for the pair Fare and Embarked.",15044
Titanic_correlation_heatmap.png,"Variables Fare and Embarked are redundant, but we can’t say the same for the pair Age and SibSp.",15045
Titanic_correlation_heatmap.png,"Variables Fare and Embarked are redundant, but we can’t say the same for the pair Pclass and Age.",15046
Titanic_correlation_heatmap.png,"Variables Age and Embarked are redundant, but we can’t say the same for the pair SibSp and Fare.",15047
Titanic_correlation_heatmap.png,"Variables Pclass and Embarked are redundant, but we can’t say the same for the pair Sex and SibSp.",15048
Titanic_correlation_heatmap.png,"Variables Sex and Age are redundant, but we can’t say the same for the pair SibSp and Parch.",15049
Titanic_correlation_heatmap.png,"Variables Pclass and Sex are redundant, but we can’t say the same for the pair Age and Fare.",15050
Titanic_correlation_heatmap.png,"Variables Sex and SibSp are redundant, but we can’t say the same for the pair Age and Embarked.",15051
Titanic_correlation_heatmap.png,"Variables Sex and Parch are redundant, but we can’t say the same for the pair Age and SibSp.",15052
Titanic_correlation_heatmap.png,"Variables Sex and Fare are redundant, but we can’t say the same for the pair Pclass and Embarked.",15053
Titanic_correlation_heatmap.png,"Variables Pclass and SibSp are redundant, but we can’t say the same for the pair Age and Fare.",15054
Titanic_correlation_heatmap.png,"Variables Pclass and Parch are redundant, but we can’t say the same for the pair Sex and SibSp.",15055
Titanic_correlation_heatmap.png,"Variables Pclass and Embarked are redundant, but we can’t say the same for the pair SibSp and Parch.",15056
Titanic_correlation_heatmap.png,"Variables Pclass and Embarked are redundant, but we can’t say the same for the pair Parch and Fare.",15057
Titanic_correlation_heatmap.png,"Variables Age and SibSp are redundant, but we can’t say the same for the pair Sex and Parch.",15058
Titanic_correlation_heatmap.png,"Variables Pclass and Sex are redundant, but we can’t say the same for the pair Age and Embarked.",15059
Titanic_correlation_heatmap.png,"Variables SibSp and Fare are redundant, but we can’t say the same for the pair Parch and Embarked.",15060
Titanic_correlation_heatmap.png,"Variables Pclass and Sex are redundant, but we can’t say the same for the pair SibSp and Fare.",15061
Titanic_correlation_heatmap.png,"Variables SibSp and Embarked are redundant, but we can’t say the same for the pair Parch and Fare.",15062
Titanic_correlation_heatmap.png,"Variables SibSp and Fare are redundant, but we can’t say the same for the pair Age and Embarked.",15063
Titanic_correlation_heatmap.png,"Variables Pclass and SibSp are redundant, but we can’t say the same for the pair Sex and Embarked.",15064
Titanic_correlation_heatmap.png,"Variables Pclass and Age are redundant, but we can’t say the same for the pair Sex and SibSp.",15065
Titanic_correlation_heatmap.png,"Variables Parch and Embarked are redundant, but we can’t say the same for the pair Sex and SibSp.",15066
Titanic_correlation_heatmap.png,"Variables Age and Embarked are redundant, but we can’t say the same for the pair Pclass and Fare.",15067
Titanic_correlation_heatmap.png,"Variables Pclass and Embarked are redundant, but we can’t say the same for the pair Age and SibSp.",15068
Titanic_correlation_heatmap.png,"Variables Parch and Fare are redundant, but we can’t say the same for the pair Sex and SibSp.",15069
Titanic_correlation_heatmap.png,"Variables Pclass and Age are redundant, but we can’t say the same for the pair SibSp and Fare.",15070
Titanic_correlation_heatmap.png,"Variables Pclass and Parch are redundant, but we can’t say the same for the pair SibSp and Embarked.",15071
Titanic_correlation_heatmap.png,"Variables Pclass and SibSp are redundant, but we can’t say the same for the pair Parch and Embarked.",15072
Titanic_correlation_heatmap.png,"Variables Age and SibSp are redundant, but we can’t say the same for the pair Parch and Embarked.",15073
Titanic_correlation_heatmap.png,"Variables Pclass and Embarked are redundant, but we can’t say the same for the pair Age and Parch.",15074
Titanic_correlation_heatmap.png,"Variables SibSp and Embarked are redundant, but we can’t say the same for the pair Sex and Fare.",15075
Titanic_correlation_heatmap.png,"Variables Age and Fare are redundant, but we can’t say the same for the pair Pclass and SibSp.",15076
Titanic_correlation_heatmap.png,"Variables Sex and Embarked are redundant, but we can’t say the same for the pair Pclass and Age.",15077
Titanic_correlation_heatmap.png,"Variables Parch and Embarked are redundant, but we can’t say the same for the pair Sex and Age.",15078
Titanic_correlation_heatmap.png,"Variables SibSp and Embarked are redundant, but we can’t say the same for the pair Sex and Age.",15079
Titanic_correlation_heatmap.png,"Variables Pclass and Parch are redundant, but we can’t say the same for the pair Sex and Fare.",15080
Titanic_correlation_heatmap.png,"Variables Sex and Age are redundant, but we can’t say the same for the pair SibSp and Embarked.",15081
Titanic_correlation_heatmap.png,"Variables Sex and Embarked are redundant, but we can’t say the same for the pair Pclass and Fare.",15082
Titanic_correlation_heatmap.png,"Variables Age and Embarked are redundant, but we can’t say the same for the pair Sex and SibSp.",15083
Titanic_correlation_heatmap.png,"Variables Sex and Parch are redundant, but we can’t say the same for the pair Age and Fare.",15084
Titanic_correlation_heatmap.png,"Variables SibSp and Fare are redundant, but we can’t say the same for the pair Pclass and Age.",15085
Titanic_correlation_heatmap.png,"Variables Pclass and Parch are redundant, but we can’t say the same for the pair Fare and Embarked.",15086
Titanic_correlation_heatmap.png,"Variables Pclass and Age are redundant, but we can’t say the same for the pair Sex and Parch.",15087
Titanic_correlation_heatmap.png,"Variables Age and Fare are redundant, but we can’t say the same for the pair SibSp and Embarked.",15088
Titanic_correlation_heatmap.png,"Variables SibSp and Embarked are redundant, but we can’t say the same for the pair Age and Fare.",15089
Titanic_correlation_heatmap.png,"Variables SibSp and Parch are redundant, but we can’t say the same for the pair Pclass and Embarked.",15090
Titanic_correlation_heatmap.png,"Variables Fare and Embarked are redundant, but we can’t say the same for the pair Age and Parch.",15091
Titanic_correlation_heatmap.png,"Variables SibSp and Parch are redundant, but we can’t say the same for the pair Pclass and Fare.",15092
Titanic_correlation_heatmap.png,"Variables Age and Embarked are redundant, but we can’t say the same for the pair Pclass and SibSp.",15093
Titanic_correlation_heatmap.png,"Variables Parch and Embarked are redundant, but we can’t say the same for the pair Pclass and Fare.",15094
Titanic_correlation_heatmap.png,"Variables SibSp and Parch are redundant, but we can’t say the same for the pair Pclass and Sex.",15095
Titanic_correlation_heatmap.png,"Variables Pclass and Embarked are redundant, but we can’t say the same for the pair Sex and Fare.",15096
Titanic_correlation_heatmap.png,"Variables Pclass and Sex are redundant, but we can’t say the same for the pair Age and SibSp.",15097
Titanic_correlation_heatmap.png,"Variables Fare and Embarked are redundant, but we can’t say the same for the pair Pclass and SibSp.",15098
Titanic_correlation_heatmap.png,"Variables Sex and Parch are redundant, but we can’t say the same for the pair Age and Embarked.",15099
Titanic_correlation_heatmap.png,"Variables Age and Parch are redundant, but we can’t say the same for the pair Sex and Fare.",15100
Titanic_correlation_heatmap.png,"Variables Parch and Fare are redundant, but we can’t say the same for the pair Pclass and Age.",15101
Titanic_correlation_heatmap.png,"Variables Sex and SibSp are redundant, but we can’t say the same for the pair Pclass and Age.",15102
Titanic_correlation_heatmap.png,"Variables Pclass and Fare are redundant, but we can’t say the same for the pair Age and SibSp.",15103
Titanic_correlation_heatmap.png,"Variables Sex and Embarked are redundant, but we can’t say the same for the pair SibSp and Fare.",15104
Titanic_correlation_heatmap.png,"Variables Fare and Embarked are redundant, but we can’t say the same for the pair Sex and Age.",15105
Titanic_correlation_heatmap.png,"Variables SibSp and Fare are redundant, but we can’t say the same for the pair Pclass and Embarked.",15106
Titanic_correlation_heatmap.png,"Variables SibSp and Fare are redundant, but we can’t say the same for the pair Sex and Age.",15107
Titanic_correlation_heatmap.png,"Variables SibSp and Parch are redundant, but we can’t say the same for the pair Pclass and Age.",15108
Titanic_correlation_heatmap.png,"Variables Pclass and Sex are redundant, but we can’t say the same for the pair Age and Parch.",15109
Titanic_correlation_heatmap.png,"Variables Pclass and Sex are redundant, but we can’t say the same for the pair Fare and Embarked.",15110
Titanic_correlation_heatmap.png,"Variables Pclass and Age are redundant, but we can’t say the same for the pair Fare and Embarked.",15111
Titanic_correlation_heatmap.png,"Variables Pclass and Sex are redundant, but we can’t say the same for the pair Parch and Fare.",15112
Titanic_correlation_heatmap.png,"Variables Pclass and SibSp are redundant, but we can’t say the same for the pair Sex and Fare.",15113
Titanic_correlation_heatmap.png,"Variables Sex and Age are redundant, but we can’t say the same for the pair Pclass and Fare.",15114
Titanic_correlation_heatmap.png,"Variables Pclass and Fare are redundant, but we can’t say the same for the pair Age and Embarked.",15115
Titanic_correlation_heatmap.png,"Variables Age and Embarked are redundant, but we can’t say the same for the pair Sex and Parch.",15116
Titanic_correlation_heatmap.png,"Variables Age and Parch are redundant, but we can’t say the same for the pair Fare and Embarked.",15117
Titanic_correlation_heatmap.png,"Variables Sex and Fare are redundant, but we can’t say the same for the pair SibSp and Parch.",15118
Titanic_correlation_heatmap.png,The variable Pclass can be discarded without risking losing information.,15119
Titanic_correlation_heatmap.png,The variable Age can be discarded without risking losing information.,15120
Titanic_correlation_heatmap.png,The variable SibSp can be discarded without risking losing information.,15121
Titanic_correlation_heatmap.png,The variable Parch can be discarded without risking losing information.,15122
Titanic_correlation_heatmap.png,The variable Fare can be discarded without risking losing information.,15123
Titanic_correlation_heatmap.png,One of the variables Age or Pclass can be discarded without losing information.,15124
Titanic_correlation_heatmap.png,One of the variables SibSp or Pclass can be discarded without losing information.,15125
Titanic_correlation_heatmap.png,One of the variables Parch or Pclass can be discarded without losing information.,15126
Titanic_correlation_heatmap.png,One of the variables Fare or Pclass can be discarded without losing information.,15127
Titanic_correlation_heatmap.png,One of the variables Pclass or Age can be discarded without losing information.,15128
Titanic_correlation_heatmap.png,One of the variables SibSp or Age can be discarded without losing information.,15129
Titanic_correlation_heatmap.png,One of the variables Parch or Age can be discarded without losing information.,15130
Titanic_correlation_heatmap.png,One of the variables Fare or Age can be discarded without losing information.,15131
Titanic_correlation_heatmap.png,One of the variables Pclass or SibSp can be discarded without losing information.,15132
Titanic_correlation_heatmap.png,One of the variables Age or SibSp can be discarded without losing information.,15133
Titanic_correlation_heatmap.png,One of the variables Parch or SibSp can be discarded without losing information.,15134
Titanic_correlation_heatmap.png,One of the variables Fare or SibSp can be discarded without losing information.,15135
Titanic_correlation_heatmap.png,One of the variables Pclass or Parch can be discarded without losing information.,15136
Titanic_correlation_heatmap.png,One of the variables Age or Parch can be discarded without losing information.,15137
Titanic_correlation_heatmap.png,One of the variables SibSp or Parch can be discarded without losing information.,15138
Titanic_correlation_heatmap.png,One of the variables Fare or Parch can be discarded without losing information.,15139
Titanic_correlation_heatmap.png,One of the variables Pclass or Fare can be discarded without losing information.,15140
Titanic_correlation_heatmap.png,One of the variables Age or Fare can be discarded without losing information.,15141
Titanic_correlation_heatmap.png,One of the variables SibSp or Fare can be discarded without losing information.,15142
Titanic_correlation_heatmap.png,One of the variables Parch or Fare can be discarded without losing information.,15143
Titanic_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,15144
Titanic_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,15145
Titanic_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,15146
Titanic_histograms_numeric.png,At least 50 of the variables present outliers.,15147
Titanic_boxplots.png,At least 60 of the variables present outliers.,15148
Titanic_histograms_numeric.png,At least 75 of the variables present outliers.,15149
Titanic_histograms_numeric.png,At least 85 of the variables present outliers.,15150
Titanic_boxplots.png,Variable Pclass presents some outliers.,15151
Titanic_histograms_numeric.png,Variable Age presents some outliers.,15152
Titanic_boxplots.png,Variable SibSp presents some outliers.,15153
Titanic_boxplots.png,Variable Parch presents some outliers.,15154
Titanic_histograms_numeric.png,Variable Fare presents some outliers.,15155
Titanic_boxplots.png,Variable Pclass doesn’t have any outliers.,15156
Titanic_histograms_numeric.png,Variable Age doesn’t have any outliers.,15157
Titanic_histograms_numeric.png,Variable SibSp doesn’t have any outliers.,15158
Titanic_boxplots.png,Variable Parch doesn’t have any outliers.,15159
Titanic_boxplots.png,Variable Fare doesn’t have any outliers.,15160
Titanic_histograms_numeric.png,Variable Pclass shows some outlier values.,15161
Titanic_boxplots.png,Variable Age shows some outlier values.,15162
Titanic_histograms_numeric.png,Variable SibSp shows some outlier values.,15163
Titanic_boxplots.png,Variable Parch shows some outlier values.,15164
Titanic_histograms_numeric.png,Variable Fare shows some outlier values.,15165
Titanic_boxplots.png,Variable Pclass shows a high number of outlier values.,15166
Titanic_histograms_numeric.png,Variable Age shows a high number of outlier values.,15167
Titanic_boxplots.png,Variable SibSp shows a high number of outlier values.,15168
Titanic_histograms_numeric.png,Variable Parch shows a high number of outlier values.,15169
Titanic_boxplots.png,Variable Fare shows a high number of outlier values.,15170
Titanic_histograms_numeric.png,Outliers seem to be a problem in the dataset.,15171
Titanic_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Pclass.",15172
Titanic_boxplots.png,"It is clear that variable SibSp shows some outliers, but we can’t be sure of the same for variable Pclass.",15173
Titanic_histograms_numeric.png,"It is clear that variable Parch shows some outliers, but we can’t be sure of the same for variable Pclass.",15174
Titanic_boxplots.png,"It is clear that variable Fare shows some outliers, but we can’t be sure of the same for variable Pclass.",15175
Titanic_boxplots.png,"It is clear that variable Pclass shows some outliers, but we can’t be sure of the same for variable Age.",15176
Titanic_boxplots.png,"It is clear that variable SibSp shows some outliers, but we can’t be sure of the same for variable Age.",15177
Titanic_histograms_numeric.png,"It is clear that variable Parch shows some outliers, but we can’t be sure of the same for variable Age.",15178
Titanic_histograms_numeric.png,"It is clear that variable Fare shows some outliers, but we can’t be sure of the same for variable Age.",15179
Titanic_boxplots.png,"It is clear that variable Pclass shows some outliers, but we can’t be sure of the same for variable SibSp.",15180
Titanic_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable SibSp.",15181
Titanic_histograms_numeric.png,"It is clear that variable Parch shows some outliers, but we can’t be sure of the same for variable SibSp.",15182
Titanic_boxplots.png,"It is clear that variable Fare shows some outliers, but we can’t be sure of the same for variable SibSp.",15183
Titanic_histograms_numeric.png,"It is clear that variable Pclass shows some outliers, but we can’t be sure of the same for variable Parch.",15184
Titanic_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Parch.",15185
Titanic_boxplots.png,"It is clear that variable SibSp shows some outliers, but we can’t be sure of the same for variable Parch.",15186
Titanic_boxplots.png,"It is clear that variable Fare shows some outliers, but we can’t be sure of the same for variable Parch.",15187
Titanic_histograms_numeric.png,"It is clear that variable Pclass shows some outliers, but we can’t be sure of the same for variable Fare.",15188
Titanic_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Fare.",15189
Titanic_histograms_numeric.png,"It is clear that variable SibSp shows some outliers, but we can’t be sure of the same for variable Fare.",15190
Titanic_histograms_numeric.png,"It is clear that variable Parch shows some outliers, but we can’t be sure of the same for variable Fare.",15191
Titanic_boxplots.png,Those boxplots show that the data is not normalized.,15192
Titanic_boxplots.png,Variable Pclass is balanced.,15193
Titanic_histograms_numeric.png,Variable Age is balanced.,15194
Titanic_boxplots.png,Variable SibSp is balanced.,15195
Titanic_histograms_numeric.png,Variable Parch is balanced.,15196
Titanic_histograms_numeric.png,Variable Fare is balanced.,15197
Titanic_histograms.png,The variable Pclass can be seen as ordinal without losing information.,15198
Titanic_histograms.png,The variable Sex can be seen as ordinal without losing information.,15199
Titanic_histograms.png,The variable Age can be seen as ordinal without losing information.,15200
Titanic_histograms.png,The variable SibSp can be seen as ordinal without losing information.,15201
Titanic_histograms.png,The variable Parch can be seen as ordinal without losing information.,15202
Titanic_histograms.png,The variable Fare can be seen as ordinal without losing information.,15203
Titanic_histograms.png,The variable Embarked can be seen as ordinal without losing information.,15204
Titanic_histograms.png,The variable Pclass can be seen as ordinal.,15205
Titanic_histograms.png,The variable Sex can be seen as ordinal.,15206
Titanic_histograms.png,The variable Age can be seen as ordinal.,15207
Titanic_histograms.png,The variable SibSp can be seen as ordinal.,15208
Titanic_histograms.png,The variable Parch can be seen as ordinal.,15209
Titanic_histograms.png,The variable Fare can be seen as ordinal.,15210
Titanic_histograms.png,The variable Embarked can be seen as ordinal.,15211
Titanic_histograms.png,"All variables, but the class, should be dealt with as numeric.",15212
Titanic_histograms.png,"All variables, but the class, should be dealt with as binary.",15213
Titanic_histograms.png,"All variables, but the class, should be dealt with as date.",15214
Titanic_histograms.png,"All variables, but the class, should be dealt with as symbolic.",15215
Titanic_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 50.,15216
Titanic_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 93.,15217
Titanic_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 47.,15218
Titanic_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 12.,15219
Titanic_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 38.,15220
Titanic_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,15221
Titanic_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",15222
Titanic_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",15223
Titanic_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",15224
Titanic_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",15225