| Logging training | |
| Running DummyClassifier() | |
| accuracy: 0.333 recall_macro: 0.333 precision_macro: 0.111 f1_macro: 0.167 | |
| === new best DummyClassifier() (using recall_macro): | |
| accuracy: 0.333 recall_macro: 0.333 precision_macro: 0.111 f1_macro: 0.167 | |
| Running GaussianNB() | |
| accuracy: 0.947 recall_macro: 0.947 precision_macro: 0.951 f1_macro: 0.946 | |
| === new best GaussianNB() (using recall_macro): | |
| accuracy: 0.947 recall_macro: 0.947 precision_macro: 0.951 f1_macro: 0.946 | |
| Running MultinomialNB() | |
| accuracy: 0.780 recall_macro: 0.780 precision_macro: 0.783 f1_macro: 0.780 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) | |
| accuracy: 0.667 recall_macro: 0.667 precision_macro: 0.500 f1_macro: 0.556 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=5) | |
| accuracy: 0.940 recall_macro: 0.940 precision_macro: 0.947 f1_macro: 0.939 | |
| Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) | |
| accuracy: 0.947 recall_macro: 0.947 precision_macro: 0.953 f1_macro: 0.946 | |
| Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) | |
| accuracy: 0.927 recall_macro: 0.927 precision_macro: 0.930 f1_macro: 0.926 | |
| Running LogisticRegression(class_weight='balanced', max_iter=1000) | |
| accuracy: 0.953 recall_macro: 0.953 precision_macro: 0.956 f1_macro: 0.953 | |
| === new best LogisticRegression(class_weight='balanced', max_iter=1000) (using recall_macro): | |
| accuracy: 0.953 recall_macro: 0.953 precision_macro: 0.956 f1_macro: 0.953 | |
| Best model: | |
| LogisticRegression(class_weight='balanced', max_iter=1000) | |
| Best Scores: | |
| accuracy: 0.953 recall_macro: 0.953 precision_macro: 0.956 f1_macro: 0.953 | |