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
import graphlab as gl

# Load the data
# The data can be downloaded using
data =  gl.SFrame.read_csv('https://static.turi.com/datasets/xgboost/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 = gl.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)

We can visualize the models using

model.show(view="Tree", tree_id=0)
model.show(view="Tree", tree_id=1)

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Advanced Features

Refer to the earlier chapters for the following features: