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회귀 트리 앙상블

랜덤 포레스트, 부스팅 및 배깅 회귀 트리

회귀 트리 앙상블은 여러 회귀 트리의 가중 조합으로 구성된 예측 모델입니다. 일반적으로, 여러 회귀 트리를 조합하면 예측 성능이 높아집니다. LSBoost를 사용하여 회귀 트리를 부스팅하려면 fitrensemble을 사용하십시오. 회귀 트리를 배깅하거나 랜덤 포레스트 [11]를 성장시키려면 fitrensemble 또는 TreeBagger를 사용하십시오. 회귀 트리 배깅을 사용하여 분위수 회귀를 구현하려면 TreeBagger를 사용하십시오.

다중클래스 분류를 위한 부스팅 분류 트리, 배깅 분류 트리, 랜덤 부분공간 앙상블 또는 오류 수정 출력 코드(ECOC) 모델과 같은 분류 앙상블에 대한 자세한 내용은 분류 앙상블을 참조하십시오.

회귀 학습기Train regression models to predict data using supervised machine learning

함수

모두 확장

fitrensembleFit ensemble of learners for regression
predictPredict responses using ensemble of regression models
oobPredictPredict out-of-bag response of ensemble
TreeBagger결정 트리의 배깅 생성하기
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

클래스

모두 확장

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

도움말 항목

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.