| Logging training | |
| Running DummyClassifier() | |
| accuracy: 0.888 recall_macro: 0.333 precision_macro: 0.296 f1_macro: 0.314 | |
| === new best DummyClassifier() (using recall_macro): | |
| accuracy: 0.888 recall_macro: 0.333 precision_macro: 0.296 f1_macro: 0.314 | |
| Running GaussianNB() | |
| accuracy: 0.636 recall_macro: 0.413 precision_macro: 0.410 f1_macro: 0.377 | |
| === new best GaussianNB() (using recall_macro): | |
| accuracy: 0.636 recall_macro: 0.413 precision_macro: 0.410 f1_macro: 0.377 | |
| Running MultinomialNB() | |
| accuracy: 0.883 recall_macro: 0.387 precision_macro: 0.438 f1_macro: 0.397 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) | |
| accuracy: 0.611 recall_macro: 0.339 precision_macro: 0.250 f1_macro: 0.250 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=5) | |
| accuracy: 0.682 recall_macro: 0.364 precision_macro: 0.346 f1_macro: 0.333 | |
| Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) | |
| accuracy: 0.720 recall_macro: 0.388 precision_macro: 0.365 f1_macro: 0.359 | |
| Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) | |
| accuracy: 0.806 recall_macro: 0.417 precision_macro: 0.392 f1_macro: 0.398 | |
| === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro): | |
| accuracy: 0.806 recall_macro: 0.417 precision_macro: 0.392 f1_macro: 0.398 | |
| Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000) | |
| accuracy: 0.802 recall_macro: 0.397 precision_macro: 0.378 f1_macro: 0.383 | |
| Best model: | |
| LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) | |
| Best Scores: | |
| accuracy: 0.806 recall_macro: 0.417 precision_macro: 0.392 f1_macro: 0.398 | |