logs.txt

Logging training
Running DummyClassifier()
accuracy: 0.491 recall_macro: 0.333 precision_macro: 0.164 f1_macro: 0.219
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.491 recall_macro: 0.333 precision_macro: 0.164 f1_macro: 0.219

Running GaussianNB()
accuracy: 0.218 recall_macro: 0.354 precision_macro: 0.473 f1_macro: 0.176
=== new best GaussianNB() (using recall_macro):
accuracy: 0.218 recall_macro: 0.354 precision_macro: 0.473 f1_macro: 0.176

Running MultinomialNB()
accuracy: 0.660 recall_macro: 0.614 precision_macro: 0.620 f1_macro: 0.612
=== new best MultinomialNB() (using recall_macro):
accuracy: 0.660 recall_macro: 0.614 precision_macro: 0.620 f1_macro: 0.612

Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.610 recall_macro: 0.460 precision_macro: 0.466 f1_macro: 0.422
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.633 recall_macro: 0.606 precision_macro: 0.634 f1_macro: 0.598
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.604 recall_macro: 0.592 precision_macro: 0.594 f1_macro: 0.574
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.693 recall_macro: 0.666 precision_macro: 0.658 f1_macro: 0.657
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.693 recall_macro: 0.666 precision_macro: 0.658 f1_macro: 0.657

Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.694 recall_macro: 0.664 precision_macro: 0.658 f1_macro: 0.656

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.693 recall_macro: 0.666 precision_macro: 0.658 f1_macro: 0.657