To interactively grow a classification tree, use the Classification Learner app. For greater flexibility, grow a classification tree using fitctree at the command line. After growing a classification tree, predict labels by passing the tree and new predictor data to predict.
| Classification Learner | Train models to classify data using supervised machine learning |
ClassificationTree | Binary decision tree for multiclass classification |
CompactClassificationTree | Compact classification tree |
ClassificationPartitionedModel | Cross-validated classification model |
Train Decision Trees Using Classification Learner App
Create and compare classification trees, and export trained models to make predictions for new data.
Supervised Learning Workflow and Algorithms
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, fitctree and
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.
Visualize Decision Surfaces of Different Classifiers
This example shows how to visualize the decision surface for different classification algorithms.
Splitting Categorical Predictors in Classification Trees
Learn about the heuristic algorithms for optimally splitting categorical variables with many levels while growing decision trees.
Improving Classification Trees and Regression Trees
Tune trees by setting name-value pair arguments in
fitctree and fitrtree.
Prediction Using Classification and Regression Trees
Predict class labels or responses using trained classification and regression trees.
Predict Out-of-Sample Responses of Subtrees
Predict responses for new data using a trained regression tree, and then plot the results.