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.

Apps

Regression Learner

Train regression models to predict data using supervised machine learning

Blocks

RegressionTree Predict

Predict responses using regression tree model (Since R2021a)

Functions

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Create Regression Tree

`fitrtree`	Fit binary decision tree for regression
`compact`	Reduce size of machine learning model
`prune`	Produce sequence of regression subtrees by pruning regression tree

Interpret Regression Tree

`lime`	Local interpretable model-agnostic explanations (LIME)
`nodeVariableRange`	Retrieve variable range of decision tree node
`partialDependence`	Compute partial dependence
`permutationImportance`	Predictor importance by permutation (Since R2024a)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`predictorImportance`	Estimates of predictor importance for regression tree
`surrogateAssociation`	Mean predictive measure of association for surrogate splits in regression tree
`shapley`	Shapley values (Since R2021a)
`view`	View regression tree

Cross-Validate Regression Tree

`crossval`	Cross-validate machine learning model
`cvloss`	Regression error by cross-validation for regression tree model
`kfoldfun`	Cross-validate function for regression
`kfoldPredict`	Predict responses for observations in cross-validated regression model
`kfoldLoss`	Loss for cross-validated partitioned regression model

Measure Performance

`loss`	Regression error for regression tree model
`resubLoss`	Resubstitution loss for regression tree model

Predict Responses

`predict`	Predict responses using regression tree model
`resubPredict`	Predict response of regression tree by resubstitution

Gather Properties of Regression Tree

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU

Use Regression Chains for Multiresponse Regression

`fitrchains`	Multiresponse regression with regression chains (Since R2024b)
`compact`	Reduce size of multiresponse regression model (Since R2024b)
`loss`	Loss for multiresponse regression model (Since R2024b)
`predict`	Predict responses using multiresponse regression model (Since R2024b)

Objects

`RegressionTree`	Regression tree
`CompactRegressionTree`	Compact regression tree
`RegressionPartitionedModel`	Cross-validated regression model
`RegressionChainEnsemble`	Multiresponse regression model (Since R2024b)
`CompactRegressionChainEnsemble`	Compact multiresponse regression model (Since R2024b)

Topics

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.
Predict Responses Using RegressionTree Predict Block
This example shows how to use the RegressionTree Predict block for response prediction in Simulink®.