Logging training
Running DummyClassifier()
accuracy: 0.333 recall_macro: 0.333 precision_macro: 0.111 f1_macro: 0.167
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.333 recall_macro: 0.333 precision_macro: 0.111 f1_macro: 0.167

Running GaussianNB()
accuracy: 0.947 recall_macro: 0.947 precision_macro: 0.951 f1_macro: 0.946
=== new best GaussianNB() (using recall_macro):
accuracy: 0.947 recall_macro: 0.947 precision_macro: 0.951 f1_macro: 0.946

Running MultinomialNB()
accuracy: 0.780 recall_macro: 0.780 precision_macro: 0.783 f1_macro: 0.780
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.667 recall_macro: 0.667 precision_macro: 0.500 f1_macro: 0.556
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.940 recall_macro: 0.940 precision_macro: 0.947 f1_macro: 0.939
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.947 recall_macro: 0.947 precision_macro: 0.953 f1_macro: 0.946
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.927 recall_macro: 0.927 precision_macro: 0.930 f1_macro: 0.926
Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.953 recall_macro: 0.953 precision_macro: 0.956 f1_macro: 0.953
=== new best LogisticRegression(class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.953 recall_macro: 0.953 precision_macro: 0.956 f1_macro: 0.953


Best model:
LogisticRegression(class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.953 recall_macro: 0.953 precision_macro: 0.956 f1_macro: 0.953