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要以交互方式生成回归树,可以使用 Regression Learner App。为了获得更大的灵活性,可以在命令行中使用 fitrtree 生成回归树。生成回归树后,可将树和新的预测变量数据传递给 predict,以预测响应。


Regression LearnerTrain regression models to predict data using supervised machine learning



fitrtreeFit binary decision tree for regression
compactCompact regression tree
pruneProduce sequence of regression subtrees by pruning
cvlossRegression error by cross validation
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predictorImportanceEstimates of predictor importance for regression tree
viewView regression 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.