Logging training
Running DummyClassifier()
accuracy: 0.632 average_precision: 0.368 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.387
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.632 average_precision: 0.368 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.387

Running GaussianNB()
accuracy: 0.947 average_precision: 0.966 roc_auc: 0.983 recall_macro: 0.933 f1_macro: 0.942
=== new best GaussianNB() (using recall_macro):
accuracy: 0.947 average_precision: 0.966 roc_auc: 0.983 recall_macro: 0.933 f1_macro: 0.942

Running MultinomialNB()
accuracy: 0.975 average_precision: 0.984 roc_auc: 0.987 recall_macro: 0.972 f1_macro: 0.973
=== new best MultinomialNB() (using recall_macro):
accuracy: 0.975 average_precision: 0.984 roc_auc: 0.987 recall_macro: 0.972 f1_macro: 0.973

Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.975 average_precision: 0.949 roc_auc: 0.972 recall_macro: 0.972 f1_macro: 0.973
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.956 average_precision: 0.919 roc_auc: 0.957 recall_macro: 0.952 f1_macro: 0.953
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.975 average_precision: 0.949 roc_auc: 0.972 recall_macro: 0.972 f1_macro: 0.973
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.975 average_precision: 0.987 roc_auc: 0.990 recall_macro: 0.972 f1_macro: 0.973
Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.975 average_precision: 0.987 roc_auc: 0.990 recall_macro: 0.972 f1_macro: 0.973

Best model:
Pipeline(steps=[('minmaxscaler', MinMaxScaler()), ('multinomialnb', MultinomialNB())])
Best Scores:
accuracy: 0.975 average_precision: 0.984 roc_auc: 0.987 recall_macro: 0.972 f1_macro: 0.973