desc_dataset.csv

Chart;description
ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition FAF <= 2.0 and the second with the condition Height <= 1.72.
ObesityDataSet_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
ObesityDataSet_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
ObesityDataSet_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
ObesityDataSet_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
ObesityDataSet_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
ObesityDataSet_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
ObesityDataSet_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
ObesityDataSet_boxplots.png;A set of boxplots of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables ['CAEC', 'CALC', 'MTRANS', 'Gender', 'family_history_with_overweight', 'FAVC', 'SMOKE', 'SCC'].
ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable NObeyesdad.
ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
ObesityDataSet_histograms_numeric.png;A set of histograms of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Family_Size <= 2.5 and the second with the condition Work_Experience <= 9.5.
customer_segmentation_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
customer_segmentation_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
customer_segmentation_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
customer_segmentation_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
customer_segmentation_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
customer_segmentation_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
customer_segmentation_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'Work_Experience', 'Family_Size'].
customer_segmentation_boxplots.png;A set of boxplots of the variables ['Age', 'Work_Experience', 'Family_Size'].
customer_segmentation_histograms_symbolic.png;A set of bar charts of the variables ['Profession', 'Spending_Score', 'Var_1', 'Gender', 'Ever_Married', 'Graduated'].
customer_segmentation_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Ever_Married', 'Graduated', 'Profession', 'Work_Experience', 'Family_Size', 'Var_1'].
customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable Segmentation.
customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
customer_segmentation_histograms_numeric.png;A set of histograms of the variables ['Age', 'Work_Experience', 'Family_Size'].
urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Age <= 0.1 and the second with the condition pH <= 5.5.
urinalysis_tests_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
urinalysis_tests_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
urinalysis_tests_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
urinalysis_tests_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
urinalysis_tests_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
urinalysis_tests_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
urinalysis_tests_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
urinalysis_tests_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'pH', 'Specific Gravity'].
urinalysis_tests_boxplots.png;A set of boxplots of the variables ['Age', 'pH', 'Specific Gravity'].
urinalysis_tests_histograms_symbolic.png;A set of bar charts of the variables ['Color', 'Transparency', 'Glucose', 'Protein', 'Epithelial Cells', 'Mucous Threads', 'Amorphous Urates', 'Bacteria', 'Gender'].
urinalysis_tests_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Color'].
urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable Diagnosis.
urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
urinalysis_tests_histograms_numeric.png;A set of histograms of the variables ['Age', 'pH', 'Specific Gravity'].
detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Ic <= 71.01 and the second with the condition Vb <= -0.37.
detect_dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
detect_dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
detect_dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
detect_dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
detect_dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
detect_dataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
detect_dataset_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
detect_dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
detect_dataset_boxplots.png;A set of boxplots of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable Output.
detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
detect_dataset_histograms_numeric.png;A set of histograms of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition BMI <= 29.85 and the second with the condition Age <= 27.5.
diabetes_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
diabetes_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
diabetes_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
diabetes_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
diabetes_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
diabetes_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
diabetes_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
diabetes_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
diabetes_boxplots.png;A set of boxplots of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
diabetes_class_histogram.png;A bar chart showing the distribution of the target variable Outcome.
diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
diabetes_histograms_numeric.png;A set of histograms of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition ssc_p <= 60.09 and the second with the condition hsc_p <= 70.24.
Placement_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Placement_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Placement_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Placement_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Placement_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Placement_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Placement_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
Placement_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
Placement_boxplots.png;A set of boxplots of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
Placement_histograms_symbolic.png;A set of bar charts of the variables ['hsc_s', 'degree_t', 'gender', 'ssc_b', 'hsc_b', 'workex', 'specialisation'].
Placement_class_histogram.png;A bar chart showing the distribution of the target variable status.
Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Placement_histograms_numeric.png;A set of histograms of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Alkphos <= 211.5 and the second with the condition Sgot <= 26.5.
Liver_Patient_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Liver_Patient_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Liver_Patient_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Liver_Patient_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Liver_Patient_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Liver_Patient_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Liver_Patient_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
Liver_Patient_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
Liver_Patient_boxplots.png;A set of boxplots of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
Liver_Patient_histograms_symbolic.png;A set of bar charts of the variables ['Gender'].
Liver_Patient_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['AG_Ratio'].
Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable Selector.
Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Liver_Patient_histograms_numeric.png;A set of histograms of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition lead_time <= 151.5 and the second with the condition no_of_special_requests <= 2.5.
Hotel_Reservations_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Hotel_Reservations_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Hotel_Reservations_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Hotel_Reservations_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Hotel_Reservations_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Hotel_Reservations_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Hotel_Reservations_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
Hotel_Reservations_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
Hotel_Reservations_boxplots.png;A set of boxplots of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
Hotel_Reservations_histograms_symbolic.png;A set of bar charts of the variables ['type_of_meal_plan', 'room_type_reserved', 'required_car_parking_space', 'arrival_year', 'repeated_guest'].
Hotel_Reservations_class_histogram.png;A bar chart showing the distribution of the target variable booking_status.
Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition basic_needs <= 3.5 and the second with the condition bullying <= 1.5.
StressLevelDataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
StressLevelDataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
StressLevelDataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
StressLevelDataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
StressLevelDataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
StressLevelDataset_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
StressLevelDataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
StressLevelDataset_boxplots.png;A set of boxplots of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
StressLevelDataset_histograms_symbolic.png;A set of bar charts of the variables ['mental_health_history'].
StressLevelDataset_class_histogram.png;A bar chart showing the distribution of the target variable stress_level.
StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
StressLevelDataset_histograms_numeric.png;A set of histograms of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition density <= 1.0 and the second with the condition chlorides <= 0.08.
WineQT_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
WineQT_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
WineQT_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
WineQT_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
WineQT_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
WineQT_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
WineQT_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
WineQT_boxplots.png;A set of boxplots of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
WineQT_class_histogram.png;A bar chart showing the distribution of the target variable quality.
WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
WineQT_histograms_numeric.png;A set of histograms of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Loan_Amount_Term <= 420.0 and the second with the condition ApplicantIncome <= 1519.0.
loan_data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
loan_data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
loan_data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
loan_data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
loan_data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
loan_data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
loan_data_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
loan_data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
loan_data_boxplots.png;A set of boxplots of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
loan_data_histograms_symbolic.png;A set of bar charts of the variables ['Dependents', 'Property_Area', 'Gender', 'Married', 'Education', 'Self_Employed', 'Credit_History'].
loan_data_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Gender', 'Dependents', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History'].
loan_data_class_histogram.png;A bar chart showing the distribution of the target variable Loan_Status.
loan_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
loan_data_histograms_numeric.png;A set of histograms of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Area <= 39172.5 and the second with the condition AspectRation <= 1.86.
Dry_Bean_Dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Dry_Bean_Dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Dry_Bean_Dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Dry_Bean_Dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Dry_Bean_Dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Dry_Bean_Dataset_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
Dry_Bean_Dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable Class.
Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition existing_credits <= 1.5 and the second with the condition residence_since <= 3.5.
credit_customers_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
credit_customers_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
credit_customers_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
credit_customers_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
credit_customers_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
credit_customers_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
credit_customers_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
credit_customers_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
credit_customers_boxplots.png;A set of boxplots of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
credit_customers_histograms_symbolic.png;A set of bar charts of the variables ['checking_status', 'employment', 'other_parties', 'other_payment_plans', 'housing', 'num_dependents', 'own_telephone', 'foreign_worker'].
credit_customers_class_histogram.png;A bar chart showing the distribution of the target variable class.
credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
credit_customers_histograms_numeric.png;A set of histograms of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Rainfall <= 0.1 and the second with the condition Pressure3pm <= 1009.65.
weatherAUS_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
weatherAUS_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
weatherAUS_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
weatherAUS_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
weatherAUS_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
weatherAUS_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
weatherAUS_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
weatherAUS_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
weatherAUS_boxplots.png;A set of boxplots of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
weatherAUS_histograms_symbolic.png;A set of bar charts of the variables ['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday'].
weatherAUS_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Rainfall', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm', 'RainToday'].
weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable RainTomorrow.
weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
weatherAUS_histograms_numeric.png;A set of histograms of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition displacement <= 1196.5 and the second with the condition height <= 1519.0.
car_insurance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
car_insurance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
car_insurance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
car_insurance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
car_insurance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
car_insurance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
car_insurance_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
car_insurance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
car_insurance_boxplots.png;A set of boxplots of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
car_insurance_histograms_symbolic.png;A set of bar charts of the variables ['area_cluster', 'segment', 'model', 'fuel_type', 'max_torque', 'max_power', 'steering_type', 'is_esc', 'is_adjustable_steering'].
car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable is_claim.
car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
car_insurance_histograms_numeric.png;A set of histograms of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition slope <= 1.5 and the second with the condition restecg <= 0.5.
heart_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
heart_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
heart_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
heart_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
heart_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
heart_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
heart_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
heart_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
heart_boxplots.png;A set of boxplots of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
heart_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'fbs', 'exang'].
heart_class_histogram.png;A bar chart showing the distribution of the target variable target.
heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
heart_histograms_numeric.png;A set of histograms of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition perimeter_mean <= 90.47 and the second with the condition texture_worst <= 27.89.
Breast_Cancer_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Breast_Cancer_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Breast_Cancer_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Breast_Cancer_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Breast_Cancer_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Breast_Cancer_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Breast_Cancer_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
Breast_Cancer_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
Breast_Cancer_boxplots.png;A set of boxplots of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable diagnosis.
Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Breast_Cancer_histograms_numeric.png;A set of histograms of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Prior_purchases <= 3.5 and the second with the condition Customer_care_calls <= 4.5.
e-commerce_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
e-commerce_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
e-commerce_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
e-commerce_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
e-commerce_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
e-commerce_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
e-commerce_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
e-commerce_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
e-commerce_boxplots.png;A set of boxplots of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
e-commerce_histograms_symbolic.png;A set of bar charts of the variables ['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender'].
e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable ReachedOnTime.
e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
e-commerce_histograms_numeric.png;A set of histograms of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Rotational speed [rpm] <= 1381.5 and the second with the condition Torque [Nm] <= 65.05.
maintenance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
maintenance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
maintenance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
maintenance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
maintenance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
maintenance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
maintenance_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
maintenance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
maintenance_boxplots.png;A set of boxplots of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
maintenance_histograms_symbolic.png;A set of bar charts of the variables ['Type', 'TWF', 'HDF', 'PWF', 'OSF', 'RNF'].
maintenance_class_histogram.png;A bar chart showing the distribution of the target variable Machine_failure.
maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
maintenance_histograms_numeric.png;A set of histograms of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Age <= 42.5 and the second with the condition NumOfProducts <= 2.5.
Churn_Modelling_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Churn_Modelling_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Churn_Modelling_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Churn_Modelling_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Churn_Modelling_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Churn_Modelling_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Churn_Modelling_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
Churn_Modelling_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
Churn_Modelling_boxplots.png;A set of boxplots of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables ['Geography', 'Gender', 'HasCrCard', 'IsActiveMember'].
Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable Exited.
Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Churn_Modelling_histograms_numeric.png;A set of histograms of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition MAJORSKEWNESS <= 74.5 and the second with the condition CIRCULARITY <= 49.5.
vehicle_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
vehicle_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
vehicle_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
vehicle_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
vehicle_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
vehicle_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
vehicle_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
vehicle_boxplots.png;A set of boxplots of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
vehicle_class_histogram.png;A bar chart showing the distribution of the target variable target.
vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
vehicle_histograms_numeric.png;A set of histograms of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition hours-per-week <= 41.5 and the second with the condition capital-loss <= 1820.5.
adult_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
adult_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
adult_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
adult_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
adult_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
adult_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
adult_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
adult_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
adult_boxplots.png;A set of boxplots of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
adult_histograms_symbolic.png;A set of bar charts of the variables ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender'].
adult_class_histogram.png;A bar chart showing the distribution of the target variable income.
adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
adult_histograms_numeric.png;A set of histograms of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition CARDIOVASCULAR <= 50.0 and the second with the condition ASHTMA <= 1.5.
Covid_Data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Covid_Data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Covid_Data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Covid_Data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Covid_Data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Covid_Data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Covid_Data_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
Covid_Data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
Covid_Data_boxplots.png;A set of boxplots of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
Covid_Data_histograms_symbolic.png;A set of bar charts of the variables ['USMER', 'SEX', 'PATIENT_TYPE'].
Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable CLASSIFICATION.
Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Covid_Data_histograms_numeric.png;A set of histograms of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition dec <= 22.21 and the second with the condition mjd <= 55090.5.
sky_survey_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
sky_survey_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
sky_survey_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
sky_survey_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
sky_survey_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
sky_survey_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
sky_survey_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
sky_survey_boxplots.png;A set of boxplots of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable class.
sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
sky_survey_histograms_numeric.png;A set of histograms of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Total phenols <= 2.36 and the second with the condition Proanthocyanins <= 1.58.
Wine_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Wine_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Wine_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Wine_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Wine_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Wine_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
Wine_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
Wine_boxplots.png;A set of boxplots of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
Wine_class_histogram.png;A bar chart showing the distribution of the target variable Class.
Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Wine_histograms_numeric.png;A set of histograms of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Hardness <= 278.29 and the second with the condition Chloramines <= 6.7.
water_potability_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
water_potability_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
water_potability_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
water_potability_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
water_potability_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
water_potability_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
water_potability_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
water_potability_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
water_potability_boxplots.png;A set of boxplots of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
water_potability_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['ph', 'Sulfate', 'Trihalomethanes'].
water_potability_class_histogram.png;A bar chart showing the distribution of the target variable Potability.
water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
water_potability_histograms_numeric.png;A set of histograms of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Height <= 0.13 and the second with the condition Diameter <= 0.45.
abalone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
abalone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
abalone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
abalone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
abalone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
abalone_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
abalone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
abalone_boxplots.png;A set of boxplots of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
abalone_class_histogram.png;A bar chart showing the distribution of the target variable Sex.
abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
abalone_histograms_numeric.png;A set of histograms of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition SMK_stat_type_cd <= 1.5 and the second with the condition gamma_GTP <= 35.5.
smoking_drinking_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
smoking_drinking_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
smoking_drinking_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
smoking_drinking_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
smoking_drinking_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
smoking_drinking_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
smoking_drinking_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
smoking_drinking_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
smoking_drinking_boxplots.png;A set of boxplots of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
smoking_drinking_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'hear_left', 'hear_right'].
smoking_drinking_class_histogram.png;A bar chart showing the distribution of the target variable DRK_YN.
smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
smoking_drinking_histograms_numeric.png;A set of histograms of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition skewness <= 5.16 and the second with the condition curtosis <= 0.19.
BankNoteAuthentication_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
BankNoteAuthentication_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
BankNoteAuthentication_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
BankNoteAuthentication_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
BankNoteAuthentication_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
BankNoteAuthentication_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
BankNoteAuthentication_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
BankNoteAuthentication_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['variance', 'skewness', 'curtosis', 'entropy'].
BankNoteAuthentication_boxplots.png;A set of boxplots of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable class.
BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
Iris_decision_tree.png;
Iris_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Iris_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Iris_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Iris_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Iris_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Iris_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
Iris_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
Iris_boxplots.png;A set of boxplots of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
Iris_class_histogram.png;A bar chart showing the distribution of the target variable Species.
Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Iris_histograms_numeric.png;A set of histograms of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition int_memory <= 30.5 and the second with the condition mobile_wt <= 91.5.
phone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
phone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
phone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
phone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
phone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
phone_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
phone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
phone_boxplots.png;A set of boxplots of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
phone_histograms_symbolic.png;A set of bar charts of the variables ['blue', 'dual_sim', 'four_g', 'three_g', 'touch_screen', 'wifi'].
phone_class_histogram.png;A bar chart showing the distribution of the target variable price_range.
phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
phone_histograms_numeric.png;A set of histograms of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Pclass <= 2.5 and the second with the condition Parch <= 0.5.
Titanic_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Titanic_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Titanic_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Titanic_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Titanic_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Titanic_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Titanic_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
Titanic_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
Titanic_boxplots.png;A set of boxplots of the variables ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
Titanic_histograms_symbolic.png;A set of bar charts of the variables ['Embarked', 'Sex'].
Titanic_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Age', 'Embarked'].
Titanic_class_histogram.png;A bar chart showing the distribution of the target variable Survived.
Titanic_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Titanic_histograms_numeric.png;A set of histograms of the variables ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Juiciness <= -0.3 and the second with the condition Crunchiness <= 2.25.
apple_quality_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
apple_quality_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
apple_quality_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
apple_quality_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
apple_quality_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
apple_quality_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
apple_quality_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
apple_quality_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
apple_quality_boxplots.png;A set of boxplots of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable Quality.
apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
apple_quality_histograms_numeric.png;A set of histograms of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition JoiningYear <= 2017.5 and the second with the condition ExperienceInCurrentDomain <= 3.5.
Employee_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Employee_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Employee_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Employee_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Employee_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Employee_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Employee_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
Employee_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].
Employee_boxplots.png;A set of boxplots of the variables ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].
Employee_histograms_symbolic.png;A set of bar charts of the variables ['Education', 'City', 'Gender', 'EverBenched'].
Employee_class_histogram.png;A bar chart showing the distribution of the target variable LeaveOrNot.
Employee_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Employee_histograms_numeric.png;A set of histograms of the variables ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].