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 [12]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.

앱

회귀 학습기

머신러닝 지도 학습을 사용하여 데이터를 예측하도록 회귀 모델 훈련시키기

블록

RegressionEnsemble Predict

Predict responses using ensemble of decision trees for regression

함수

모두 확장

회귀 앙상블

회귀 앙상블 만들기

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

회귀 앙상블 수정하기

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

회귀 앙상블 교차 검증하기

`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

성능 측정하기

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

관측값 분류하기

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

회귀 앙상블의 속성 수집하기

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

배깅 회귀 트리

`fitrensemble`	Fit ensemble of learners for regression
`TreeBagger`	배깅 결정 트리의 앙상블
`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

회귀 모델 개선하기

crossval Cross validate ensemble

회귀 모델 해석하기

`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

클래스

모두 확장

회귀 앙상블

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

배깅 회귀 트리

`TreeBagger`	배깅 결정 트리의 앙상블
`CompactTreeBagger`	Compact ensemble of bagged decision trees
`RegressionBaggedEnsemble`	Regression 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.
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.