回归树集成

随机森林、提升回归树和装袋回归树

回归树集成是由多个回归树的加权组合构成的预测模型。通常，组合多个回归树可以提高预测性能。要使用 LSBoost 提升回归树，可以使用 fitrensemble。要使用装袋法组合回归树或要生成随机森林 [12]，可以使用 fitrensemble 或 TreeBagger。要使用装袋回归树实现分位数回归，可以使用 TreeBagger。

对于分类集成，例如提升分类树或装袋分类树、随机子空间集成或用于多分类的纠错输出编码 (ECOC) 模型，请参阅分类集成。

App

回归学习器

使用有监督机器学习训练回归模型来预测数据

模块

RegressionEnsemble Predict

Predict responses using ensemble of decision trees for regression

函数

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回归集成

创建回归集成

`fitrensemble`	Fit ensemble of learners for regression
`compact`	Create compact regression ensemble
`fitensemble`	Fit ensemble of learners for classification and regression

修改回归集成

`regularize`	Find weights to minimize resubstitution error plus penalty term
`removeLearners`	Remove members of compact regression ensemble
`resume`	Resume training ensemble
`shrink`	Prune ensemble

交叉验证回归集成

`cvshrink`	Cross-validate shrinking (pruning) ensemble
`kfoldLoss`	Loss for cross-validated partitioned regression model
`kfoldPredict`	Predict responses for observations in cross-validated regression model
`kfoldfun`	Cross-validate function for regression

测量性能

`loss`	Regression error
`resubLoss`	Regression error by resubstitution

为观测值分类

`predict`	Predict responses using ensemble of regression models
`resubPredict`	Predict response of ensemble by resubstitution

收集回归集成的属性

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

装袋回归树

`fitrensemble`	Fit ensemble of learners for regression
`TreeBagger`	Ensemble of bagged decision trees
`predict`	Predict responses using ensemble of bagged decision trees
`oobPredict`	Ensemble predictions for out-of-bag observations
`quantilePredict`	Predict response quantile using bag of regression trees
`oobQuantilePredict`	Quantile predictions for out-of-bag observations from bag of regression trees

改进回归

crossval Cross validate ensemble

解释回归

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`predictorImportance`	Estimates of predictor importance for regression ensemble
`shapley`	Shapley values

类

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回归集成

`RegressionEnsemble`	Ensemble regression
`CompactRegressionEnsemble`	Compact regression ensemble class
`RegressionPartitionedEnsemble`	Cross-validated regression ensemble

装袋回归树

`TreeBagger`	Ensemble of bagged decision trees
`CompactTreeBagger`	Compact ensemble of bagged decision trees
`RegressionBaggedEnsemble`	Regression ensemble grown by resampling

主题

Ensemble Algorithms
Learn about different algorithms for ensemble learning.
Framework for Ensemble Learning
Obtain highly accurate predictions by using many weak learners.
Train Regression Ensemble
Train a simple regression ensemble.
Test Ensemble Quality
Learn methods to evaluate the predictive quality of an ensemble.
Select Predictors for Random Forests
Select split-predictors for random forests using interaction test algorithm.
Ensemble Regularization
Automatically choose fewer weak learners for an ensemble in a way that does not diminish predictive performance.
Bootstrap Aggregation (Bagging) of Regression Trees Using TreeBagger
Create a TreeBagger ensemble for regression.
Use Parallel Processing for Regression TreeBagger Workflow
Speed up computation by running TreeBagger in parallel.
Detect Outliers Using Quantile Regression
Detect outliers in data using quantile random forest.
Conditional Quantile Estimation Using Kernel Smoothing
Estimate conditional quantiles of a response given predictor data using quantile random forest and by estimating the conditional distribution function of the response using kernel smoothing.
Tune Random Forest Using Quantile Error and Bayesian Optimization
Tune quantile random forest using Bayesian optimization.
Predict Responses Using RegressionEnsemble Predict Block
Train a regression ensemble model with optimal hyperparameters, and then use the RegressionEnsemble Predict block for response prediction.
Time Series Forecasting Using Ensemble of Boosted Regression Trees
Perform single-step and multiple-step time series forecasting with an ensemble of boosted regression trees.