本页对应的英文页面已更新,但尚未翻译。 若要查看最新内容,请点击此处访问英文页面。

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 [11],可以使用 fitrensembleTreeBagger。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

Regression LearnerTrain regression models to predict data using supervised machine learning

函数

全部展开

fitrensembleFit ensemble of learners for regression
predictPredict responses using ensemble of regression models
oobPredictPredict out-of-bag response of ensemble
TreeBaggerCreate bag of decision trees
fitrensembleFit ensemble of learners for regression
predictPredict responses using ensemble of bagged decision trees
oobPredictEnsemble predictions for out-of-bag observations
quantilePredictPredict response quantile using bag of regression trees
oobQuantilePredictQuantile predictions for out-of-bag observations from bag of regression trees
crossvalCross validate ensemble
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predictorImportanceEstimates of predictor importance for regression ensemble

全部展开

RegressionEnsembleEnsemble regression
CompactRegressionEnsembleCompact regression ensemble class
RegressionPartitionedEnsembleCross-validated regression ensemble
TreeBaggerBag of decision trees
CompactTreeBaggerCompact ensemble of decision trees grown by bootstrap aggregation
RegressionBaggedEnsembleRegression 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.