Logging training Running DummyClassifier() accuracy: 0.513 average_precision: 0.487 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.339 === new best DummyClassifier() (using recall_macro): accuracy: 0.513 average_precision: 0.487 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.339 Running GaussianNB() accuracy: 0.592 average_precision: 0.669 roc_auc: 0.824 recall_macro: 0.602 f1_macro: 0.534 === new best GaussianNB() (using recall_macro): accuracy: 0.592 average_precision: 0.669 roc_auc: 0.824 recall_macro: 0.602 f1_macro: 0.534 Running MultinomialNB() accuracy: 0.857 average_precision: 0.934 roc_auc: 0.931 recall_macro: 0.856 f1_macro: 0.856 === new best MultinomialNB() (using recall_macro): accuracy: 0.857 average_precision: 0.934 roc_auc: 0.931 recall_macro: 0.856 f1_macro: 0.856 Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) accuracy: 0.749 average_precision: 0.680 roc_auc: 0.749 recall_macro: 0.749 f1_macro: 0.749 Running DecisionTreeClassifier(class_weight='balanced', max_depth=5) accuracy: 0.883 average_precision: 0.943 roc_auc: 0.940 recall_macro: 0.882 f1_macro: 0.882 === new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro): accuracy: 0.883 average_precision: 0.943 roc_auc: 0.940 recall_macro: 0.882 f1_macro: 0.882 Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) accuracy: 0.833 average_precision: 0.857 roc_auc: 0.878 recall_macro: 0.832 f1_macro: 0.833 Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) accuracy: 0.873 average_precision: 0.941 roc_auc: 0.060 recall_macro: 0.872 f1_macro: 0.873 Running LogisticRegression(class_weight='balanced', max_iter=1000) accuracy: 0.886 average_precision: 0.949 roc_auc: 0.051 recall_macro: 0.885 f1_macro: 0.886 === new best LogisticRegression(class_weight='balanced', max_iter=1000) (using recall_macro): accuracy: 0.886 average_precision: 0.949 roc_auc: 0.051 recall_macro: 0.885 f1_macro: 0.886 Best model: LogisticRegression(class_weight='balanced', max_iter=1000) Best Scores: accuracy: 0.886 average_precision: 0.949 roc_auc: 0.051 recall_macro: 0.885 f1_macro: 0.886