logs.txt

Logging training
Running DummyClassifier()
accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209

Running GaussianNB()
accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209
Running MultinomialNB()
accuracy: 0.456 recall_macro: 0.333 precision_macro: 0.152 f1_macro: 0.209
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.354 recall_macro: 0.345 precision_macro: 0.226 f1_macro: 0.268
=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=1) (using recall_macro):
accuracy: 0.354 recall_macro: 0.345 precision_macro: 0.226 f1_macro: 0.268

Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.360 recall_macro: 0.352 precision_macro: 0.352 f1_macro: 0.339
=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro):
accuracy: 0.360 recall_macro: 0.352 precision_macro: 0.352 f1_macro: 0.339

Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.245 recall_macro: 0.333 precision_macro: 0.082 f1_macro: 0.131
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278

Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.361 recall_macro: 0.353 precision_macro: 0.241 f1_macro: 0.278