Logging training
Running DummyClassifier()
accuracy: 0.894 recall_macro: 0.333 precision_macro: 0.298 f1_macro: 0.315
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.894 recall_macro: 0.333 precision_macro: 0.298 f1_macro: 0.315

Running GaussianNB()
accuracy: 0.650 recall_macro: 0.492 precision_macro: 0.389 f1_macro: 0.375
=== new best GaussianNB() (using recall_macro):
accuracy: 0.650 recall_macro: 0.492 precision_macro: 0.389 f1_macro: 0.375

Running MultinomialNB()
accuracy: 0.892 recall_macro: 0.342 precision_macro: 0.443 f1_macro: 0.334
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.820 recall_macro: 0.428 precision_macro: 0.320 f1_macro: 0.336
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.572 recall_macro: 0.512 precision_macro: 0.387 f1_macro: 0.349
=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro):
accuracy: 0.572 recall_macro: 0.512 precision_macro: 0.387 f1_macro: 0.349

Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.649 recall_macro: 0.530 precision_macro: 0.391 f1_macro: 0.377
=== new best DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) (using recall_macro):
accuracy: 0.649 recall_macro: 0.530 precision_macro: 0.391 f1_macro: 0.377

Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.733 recall_macro: 0.630 precision_macro: 0.440 f1_macro: 0.456
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.733 recall_macro: 0.630 precision_macro: 0.440 f1_macro: 0.456

Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
accuracy: 0.749 recall_macro: 0.617 precision_macro: 0.442 f1_macro: 0.462

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.733 recall_macro: 0.630 precision_macro: 0.440 f1_macro: 0.456