回归树

用于回归的二叉决策树

要以交互方式生成回归树，可以使用回归学习器。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。

App

使用有监督机器学习训练回归模型来预测数据

Predict responses using regression tree model (自 R2021a 起)

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

`lime`	Local interpretable model-agnostic explanations (LIME) (自 R2020b 起)
`nodeVariableRange`	Retrieve variable range of decision tree node (自 R2020a 起)
`partialDependence`	Compute partial dependence (自 R2020b 起)
`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 (自 R2021a 起)
`view`	View regression tree

`crossval`
`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

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

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

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU (自 R2020b 起)

`RegressionTree`	Regression tree
`CompactRegressionTree`	Compact regression tree
`RegressionPartitionedModel`	Cross-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.
查看决策树
创建并查看已训练的决策树的文本或图描述。
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®.