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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 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

回归学习器Train regression models to predict data using supervised machine learning

模块

RegressionEnsemble PredictPredict responses using ensemble of decision trees for regression

函数

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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
limeLocal interpretable model-agnostic explanations (LIME)
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predictorImportanceEstimates of predictor importance for regression ensemble
shapleyShapley values

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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