logs.txt

Logging training
Running DummyClassifier()
accuracy: 0.894 recall_macro: 0.333 precision_macro: 0.298 f1_macro: 0.315
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.894 recall_macro: 0.333 precision_macro: 0.298 f1_macro: 0.315

Running GaussianNB()
accuracy: 0.653 recall_macro: 0.513 precision_macro: 0.392 f1_macro: 0.381
=== new best GaussianNB() (using recall_macro):
accuracy: 0.653 recall_macro: 0.513 precision_macro: 0.392 f1_macro: 0.381

Running MultinomialNB()
accuracy: 0.893 recall_macro: 0.344 precision_macro: 0.464 f1_macro: 0.336
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.820 recall_macro: 0.428 precision_macro: 0.320 f1_macro: 0.336
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.562 recall_macro: 0.514 precision_macro: 0.384 f1_macro: 0.343
=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro):
accuracy: 0.562 recall_macro: 0.514 precision_macro: 0.384 f1_macro: 0.343

Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.535 recall_macro: 0.519 precision_macro: 0.382 f1_macro: 0.334
=== new best DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) (using recall_macro):
accuracy: 0.535 recall_macro: 0.519 precision_macro: 0.382 f1_macro: 0.334

Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.736 recall_macro: 0.632 precision_macro: 0.440 f1_macro: 0.458
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.736 recall_macro: 0.632 precision_macro: 0.440 f1_macro: 0.458

Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
accuracy: 0.745 recall_macro: 0.619 precision_macro: 0.441 f1_macro: 0.461

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.736 recall_macro: 0.632 precision_macro: 0.440 f1_macro: 0.458