Logging training
Running DummyClassifier()
accuracy: 0.627 average_precision: 0.373 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.386
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.627 average_precision: 0.373 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.386

Running GaussianNB()
accuracy: 0.924 average_precision: 0.974 roc_auc: 0.984 recall_macro: 0.920 f1_macro: 0.919
=== new best GaussianNB() (using recall_macro):
accuracy: 0.924 average_precision: 0.974 roc_auc: 0.984 recall_macro: 0.920 f1_macro: 0.919

Running MultinomialNB()
accuracy: 0.840 average_precision: 0.914 roc_auc: 0.927 recall_macro: 0.787 f1_macro: 0.807
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.900 average_precision: 0.797 roc_auc: 0.898 recall_macro: 0.898 f1_macro: 0.894
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.940 average_precision: 0.881 roc_auc: 0.938 recall_macro: 0.938 f1_macro: 0.936
=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro):
accuracy: 0.940 average_precision: 0.881 roc_auc: 0.938 recall_macro: 0.938 f1_macro: 0.936

Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.937 average_precision: 0.884 roc_auc: 0.933 recall_macro: 0.936 f1_macro: 0.933
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.979 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.977 f1_macro: 0.977
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.979 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.977 f1_macro: 0.977

Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.968 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.967 f1_macro: 0.966

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.979 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.977 f1_macro: 0.977