| Logging training | |
| Running DummyClassifier() | |
| accuracy: 0.788 average_precision: 0.212 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.441 | |
| === new best DummyClassifier() (using recall_macro): | |
| accuracy: 0.788 average_precision: 0.212 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.441 | |
| Running GaussianNB() | |
| accuracy: 0.688 average_precision: 0.405 roc_auc: 0.802 recall_macro: 0.802 f1_macro: 0.665 | |
| === new best GaussianNB() (using recall_macro): | |
| accuracy: 0.688 average_precision: 0.405 roc_auc: 0.802 recall_macro: 0.802 f1_macro: 0.665 | |
| Running MultinomialNB() | |
| accuracy: 0.978 average_precision: 0.990 roc_auc: 0.997 recall_macro: 0.967 f1_macro: 0.967 | |
| === new best MultinomialNB() (using recall_macro): | |
| accuracy: 0.978 average_precision: 0.990 roc_auc: 0.997 recall_macro: 0.967 f1_macro: 0.967 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) | |
| accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000 | |
| === new best DecisionTreeClassifier(class_weight='balanced', max_depth=1) (using recall_macro): | |
| accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000 | |
| Running DecisionTreeClassifier(class_weight='balanced', max_depth=5) | |
| accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000 | |
| Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) | |
| accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000 | |
| Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) | |
| accuracy: 0.999 average_precision: 1.000 roc_auc: 0.000 recall_macro: 0.999 f1_macro: 0.999 | |
| Running LogisticRegression(class_weight='balanced', max_iter=1000) | |
| accuracy: 1.000 average_precision: 1.000 roc_auc: 0.000 recall_macro: 1.000 f1_macro: 1.000 | |
| Best model: | |
| DecisionTreeClassifier(class_weight='balanced', max_depth=1) | |
| Best Scores: | |
| accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000 | |