要以交互方式生成分类树，可以使用 Classification Learner App。为了获得更大的灵活性，可以在命令行中使用
|Cross-validated decision tree|
|Classification edge for observations not used for training|
|Classification loss for observations not used for training|
|Cross validate function|
|Classification margins for observations not used for training|
|Predict response for observations not used for training|
Create and compare classification trees, and export trained models to make predictions for new data.
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
Understand decision trees and how to fit them to data.
To grow decision trees,
fitrtree apply the standard CART algorithm by default to
the training data.
Create and view a text or graphic description of a trained decision tree.
This example shows how to visualize the decision surface for different classification algorithms.
Learn about the heuristic algorithms for optimally splitting categorical variables with many levels while growing decision trees.
Tune trees by setting name-value pair arguments in
Predict class labels or responses using trained classification and regression trees.
Predict responses for new data using a trained regression tree, and then plot the results.