logs.txt

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