A decision tree classifier is a simple machine learning model suitable for getting started with classification tasks. Refer to the chapter on decision tree regression for background on decision trees.
import turicreate as tc # Load the data data = tc.SFrame.read_csv('https://raw.githubusercontent.com/apple/turicreate/master/src/python/turicreate/test/mushroom.csv') # Label 'c' is edible data['label'] = data['label'] == 'c' # Make a train-test split train_data, test_data = data.random_split(0.8) # Create a model. model = tc.decision_tree_classifier.create(train_data, target='label', max_depth = 3) # Save predictions to an SArray predictions = model.predict(test_data) # Evaluate the model and save the results into a dictionary results = model.evaluate(test_data)
Refer to the earlier chapters for the following features: