Logging training Running DummyClassifier() accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209 === new best DummyClassifier() (using recall_macro): accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209 Running GaussianNB() accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209 Running MultinomialNB() accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209 Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) accuracy: 0.354 recall_macro: 0.345 precision_macro: 0.226 f1_macro: 0.268 === new best DecisionTreeClassifier(class_weight='balanced', max_depth=1) (using recall_macro): accuracy: 0.354 recall_macro: 0.345 precision_macro: 0.226 f1_macro: 0.268 Running DecisionTreeClassifier(class_weight='balanced', max_depth=5) accuracy: 0.360 recall_macro: 0.352 precision_macro: 0.352 f1_macro: 0.339 === new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro): accuracy: 0.360 recall_macro: 0.352 precision_macro: 0.352 f1_macro: 0.339 Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) accuracy: 0.245 recall_macro: 0.333 precision_macro: 0.082 f1_macro: 0.131 Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278 === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro): accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278 Running LogisticRegression(class_weight='balanced', max_iter=1000) accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278 Best model: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) Best Scores: accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278