test_models.py

# test the accuracy of each model using default hyperparameters
results = []
names = []
scoring = 'accuracy'
for name, model in models:
    # fit the model with the training data
    model.fit(X_train, y_train).predict(X_test)
    # make predictions with the testing data
    predictions = model.predict(X_test)
    # calculate accuracy 
    accuracy = accuracy_score(y_test, predictions)
    # append the model name and the accuracy to the lists
    results.append(accuracy)
    names.append(name)
    # print classifier accuracy
    print('Classifier: {}, Accuracy: {})'.format(name, accuracy))