| Logging training | |
| Running DummyClassifier() | |
| accuracy: 0.495 recall_macro: 0.100 precision_macro: 0.050 f1_macro: 0.066 | |
| === new best DummyClassifier() (using recall_macro): | |
| accuracy: 0.495 recall_macro: 0.100 precision_macro: 0.050 f1_macro: 0.066 | |
| Running GaussianNB() | |
| accuracy: 0.830 recall_macro: 0.537 precision_macro: 0.432 f1_macro: 0.400 | |
| === new best GaussianNB() (using recall_macro): | |
| accuracy: 0.830 recall_macro: 0.537 precision_macro: 0.432 f1_macro: 0.400 | |
| Running MultinomialNB() | |
| accuracy: 0.900 recall_macro: 0.579 precision_macro: 0.510 f1_macro: 0.514 | |
| === new best MultinomialNB() (using recall_macro): | |
| accuracy: 0.900 recall_macro: 0.579 precision_macro: 0.510 f1_macro: 0.514 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) | |
| accuracy: 0.400 recall_macro: 0.200 precision_macro: 0.113 f1_macro: 0.124 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=10) | |
| accuracy: 0.909 recall_macro: 0.641 precision_macro: 0.531 f1_macro: 0.520 | |
| === new best DecisionTreeClassifier(class_weight='balanced', max_depth=10) (using recall_macro): | |
| accuracy: 0.909 recall_macro: 0.641 precision_macro: 0.531 f1_macro: 0.520 | |
| Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) | |
| accuracy: 0.931 recall_macro: 0.723 precision_macro: 0.563 f1_macro: 0.595 | |
| === new best DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) (using recall_macro): | |
| accuracy: 0.931 recall_macro: 0.723 precision_macro: 0.563 f1_macro: 0.595 | |
| Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) | |
| accuracy: 0.946 recall_macro: 0.739 precision_macro: 0.614 f1_macro: 0.647 | |
| === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro): | |
| accuracy: 0.946 recall_macro: 0.739 precision_macro: 0.614 f1_macro: 0.647 | |
| Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000) | |
| accuracy: 0.948 recall_macro: 0.749 precision_macro: 0.623 f1_macro: 0.657 | |
| === new best LogisticRegression(C=1, class_weight='balanced', max_iter=1000) (using recall_macro): | |
| accuracy: 0.948 recall_macro: 0.749 precision_macro: 0.623 f1_macro: 0.657 | |
| Best model: | |
| LogisticRegression(C=1, class_weight='balanced', max_iter=1000) | |
| Best Scores: | |
| accuracy: 0.948 recall_macro: 0.749 precision_macro: 0.623 f1_macro: 0.657 | |