Logging training Running DummyClassifier() accuracy: 0.491 recall_macro: 0.333 precision_macro: 0.164 f1_macro: 0.219 === new best DummyClassifier() (using recall_macro): accuracy: 0.491 recall_macro: 0.333 precision_macro: 0.164 f1_macro: 0.219 Running GaussianNB() accuracy: 0.218 recall_macro: 0.354 precision_macro: 0.473 f1_macro: 0.176 === new best GaussianNB() (using recall_macro): accuracy: 0.218 recall_macro: 0.354 precision_macro: 0.473 f1_macro: 0.176 Running MultinomialNB() accuracy: 0.660 recall_macro: 0.614 precision_macro: 0.620 f1_macro: 0.612 === new best MultinomialNB() (using recall_macro): accuracy: 0.660 recall_macro: 0.614 precision_macro: 0.620 f1_macro: 0.612 Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) accuracy: 0.610 recall_macro: 0.460 precision_macro: 0.466 f1_macro: 0.422 Running DecisionTreeClassifier(class_weight='balanced', max_depth=5) accuracy: 0.633 recall_macro: 0.606 precision_macro: 0.634 f1_macro: 0.598 Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) accuracy: 0.604 recall_macro: 0.592 precision_macro: 0.594 f1_macro: 0.574 Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) accuracy: 0.693 recall_macro: 0.666 precision_macro: 0.658 f1_macro: 0.657 === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro): accuracy: 0.693 recall_macro: 0.666 precision_macro: 0.658 f1_macro: 0.657 Running LogisticRegression(class_weight='balanced', max_iter=1000) accuracy: 0.694 recall_macro: 0.664 precision_macro: 0.658 f1_macro: 0.656 Best model: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) Best Scores: accuracy: 0.693 recall_macro: 0.666 precision_macro: 0.658 f1_macro: 0.657