Logging training
Running DummyClassifier()
accuracy: 0.788 average_precision: 0.212 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.441
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.788 average_precision: 0.212 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.441

Running GaussianNB()
accuracy: 0.688 average_precision: 0.405 roc_auc: 0.802 recall_macro: 0.802 f1_macro: 0.665
=== new best GaussianNB() (using recall_macro):
accuracy: 0.688 average_precision: 0.405 roc_auc: 0.802 recall_macro: 0.802 f1_macro: 0.665

Running MultinomialNB()
accuracy: 0.978 average_precision: 0.990 roc_auc: 0.997 recall_macro: 0.967 f1_macro: 0.967
=== new best MultinomialNB() (using recall_macro):
accuracy: 0.978 average_precision: 0.990 roc_auc: 0.997 recall_macro: 0.967 f1_macro: 0.967

Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=1) (using recall_macro):
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000

Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.999 average_precision: 1.000 roc_auc: 0.000 recall_macro: 0.999 f1_macro: 0.999
Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 1.000 average_precision: 1.000 roc_auc: 0.000 recall_macro: 1.000 f1_macro: 1.000

Best model:
DecisionTreeClassifier(class_weight='balanced', max_depth=1)
Best Scores:
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000