logs.txt

Logging training
Running DummyClassifier()
accuracy: 0.888 recall_macro: 0.333 precision_macro: 0.296 f1_macro: 0.314
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.888 recall_macro: 0.333 precision_macro: 0.296 f1_macro: 0.314

Running GaussianNB()
accuracy: 0.636 recall_macro: 0.413 precision_macro: 0.410 f1_macro: 0.377
=== new best GaussianNB() (using recall_macro):
accuracy: 0.636 recall_macro: 0.413 precision_macro: 0.410 f1_macro: 0.377

Running MultinomialNB()
accuracy: 0.883 recall_macro: 0.387 precision_macro: 0.438 f1_macro: 0.397
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.611 recall_macro: 0.339 precision_macro: 0.250 f1_macro: 0.250
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.682 recall_macro: 0.364 precision_macro: 0.346 f1_macro: 0.333
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.720 recall_macro: 0.388 precision_macro: 0.365 f1_macro: 0.359
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.806 recall_macro: 0.417 precision_macro: 0.392 f1_macro: 0.398
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.806 recall_macro: 0.417 precision_macro: 0.392 f1_macro: 0.398

Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
accuracy: 0.802 recall_macro: 0.397 precision_macro: 0.378 f1_macro: 0.383

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.806 recall_macro: 0.417 precision_macro: 0.392 f1_macro: 0.398