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Regression Trees

Binary decision trees for regression

To interactively grow a regression tree, use the Regression Learner app. For greater flexibility, grow a regression tree using fitrtree at the command line. After growing a regression tree, predict responses by passing the tree and new predictor data to predict.


Regression LearnerTrain regression models to predict data using supervised machine learning


expand all

fitrtreeFit binary decision tree for regression
compactCompact regression tree
pruneProduce sequence of subtrees by pruning
cvlossRegression error by cross validation
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predictorImportanceEstimates of predictor importance
viewView tree
crossvalCross-validated decision tree
kfoldfunCross validate function
kfoldPredictPredict response for observations not used for training
kfoldLossCross-validation loss of partitioned regression model
lossRegression error
resubLossRegression error by resubstitution
predictPredict responses using regression tree
resubPredictPredict resubstitution response of tree


RegressionTreeRegression tree
CompactRegressionTreeCompact regression tree
RegressionPartitionedModelCross-validated regression model


Train Regression Trees Using Regression Learner App

Create and compare regression 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.

Decision Trees

Understand decision trees and how to fit them to data.

Growing Decision Trees

To grow decision trees, fitctree and fitrtree apply the standard CART algorithm by default to the training data.

View Decision Tree

Create and view a text or graphic description of a trained decision tree.

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.