python from milk import defaultlearner from milk import classification_dataset data = [ (['good', 'bad'], 'positive'), (['bad', 'good'], 'negative'), (['good', 'good'], 'positive'), (['bad', 'bad'], 'negative') ] features = ['feature1', 'feature2'] dataset = classification_dataset(features=features, data=data) classifier = defaultlearner() model = classifier.train(dataset) test_data = ['good', 'bad'] prediction = model.apply(test_data) print(prediction)