Logging training
Running DummyClassifier()
accuracy: 0.643 average_precision: 0.357 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.392
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.643 average_precision: 0.357 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.392

Running GaussianNB()
accuracy: 0.586 average_precision: 0.499 roc_auc: 0.618 recall_macro: 0.547 f1_macro: 0.494
=== new best GaussianNB() (using recall_macro):
accuracy: 0.586 average_precision: 0.499 roc_auc: 0.618 recall_macro: 0.547 f1_macro: 0.494

Running MultinomialNB()
accuracy: 0.647 average_precision: 0.508 roc_auc: 0.626 recall_macro: 0.590 f1_macro: 0.585
=== new best MultinomialNB() (using recall_macro):
accuracy: 0.647 average_precision: 0.508 roc_auc: 0.626 recall_macro: 0.590 f1_macro: 0.585

Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.586 average_precision: 0.396 roc_auc: 0.562 recall_macro: 0.562 f1_macro: 0.542
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.566 average_precision: 0.382 roc_auc: 0.529 recall_macro: 0.531 f1_macro: 0.522
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.553 average_precision: 0.412 roc_auc: 0.552 recall_macro: 0.561 f1_macro: 0.536
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.615 average_precision: 0.509 roc_auc: 0.390 recall_macro: 0.589 f1_macro: 0.582
Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.574 average_precision: 0.501 roc_auc: 0.397 recall_macro: 0.551 f1_macro: 0.544

Best model:
Pipeline(steps=[('minmaxscaler', MinMaxScaler()), ('multinomialnb', MultinomialNB())])
Best Scores:
accuracy: 0.647 average_precision: 0.508 roc_auc: 0.626 recall_macro: 0.590 f1_macro: 0.585