Decision Tree Classifier

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.

Introductory Example

In this example, we will use the Mushrooms dataset.¹

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)

Advanced Features

Refer to the earlier chapters for the following features:

• Using categorical features
• Sparse features
• List features
• Evaluating Results
• Multiclass Classification
• Working with imbalanced data