# Random Forest Classifier

A Random Forest classifier is one of the most effective machine learning models for predictive analytics. Refer to the chapter on random forest regression for background on random forests.

##### Introductory Example

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

import turicreate as tc

# 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.random_forest_classifier.create(train_data, target='label',
max_iterations=2,
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)

See the chapter on random forest regression for additional tips and tricks of using the random forest classifier model.

Refer to the earlier chapters for the following features: