Logging training
Running DummyClassifier()
accuracy: 0.495 recall_macro: 0.100 precision_macro: 0.050 f1_macro: 0.066
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.495 recall_macro: 0.100 precision_macro: 0.050 f1_macro: 0.066

Running GaussianNB()
accuracy: 0.830 recall_macro: 0.537 precision_macro: 0.432 f1_macro: 0.400
=== new best GaussianNB() (using recall_macro):
accuracy: 0.830 recall_macro: 0.537 precision_macro: 0.432 f1_macro: 0.400

Running MultinomialNB()
accuracy: 0.900 recall_macro: 0.579 precision_macro: 0.510 f1_macro: 0.514
=== new best MultinomialNB() (using recall_macro):
accuracy: 0.900 recall_macro: 0.579 precision_macro: 0.510 f1_macro: 0.514

Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.400 recall_macro: 0.200 precision_macro: 0.113 f1_macro: 0.124
Running DecisionTreeClassifier(class_weight='balanced', max_depth=10)
accuracy: 0.909 recall_macro: 0.641 precision_macro: 0.531 f1_macro: 0.520
=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=10) (using recall_macro):
accuracy: 0.909 recall_macro: 0.641 precision_macro: 0.531 f1_macro: 0.520

Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.931 recall_macro: 0.723 precision_macro: 0.563 f1_macro: 0.595
=== new best DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) (using recall_macro):
accuracy: 0.931 recall_macro: 0.723 precision_macro: 0.563 f1_macro: 0.595

Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.946 recall_macro: 0.739 precision_macro: 0.614 f1_macro: 0.647
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.946 recall_macro: 0.739 precision_macro: 0.614 f1_macro: 0.647

Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
accuracy: 0.948 recall_macro: 0.749 precision_macro: 0.623 f1_macro: 0.657
=== new best LogisticRegression(C=1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.948 recall_macro: 0.749 precision_macro: 0.623 f1_macro: 0.657


Best model:
LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.948 recall_macro: 0.749 precision_macro: 0.623 f1_macro: 0.657