Regressionsbaum-Ensembles

Random-Forest-Regressionsbäume sowie Boosted- und Bagged-Regressionsbäume

Ein Regressionsbaum-Ensemble ist ein prädiktives Modell, das aus einer gewichteten Kombination mehrerer Regressionsbäume besteht. Im Allgemeinen steigert die Kombination mehrerer Regressionsbäume die prädiktive Leistung. Verwenden Sie fitrensemble, um Regressionsbäume mithilfe von LSBoost zu verstärken. Verwenden Sie fitrensemble oder TreeBagger, um Regressionsbäume zu baggen oder einen Random Forest aufzubauen. Verwenden Sie TreeBagger, um eine Quantil-Regression mithilfe eines Regressionsbaum-Bags zu implementieren.

Weitere Informationen über Klassifizierungs-Ensembles wie Boosted- oder Bagged-Klassifizierungsbäume, zufällige Subspace-Ensembles oder ECOC-Modelle (Error-Correcting Output Codes, fehlerkorrigierende Ausgangscodes) für Multiklassen-Klassifizierungen finden Sie unter Klassifizierungs-Ensembles.

Apps

Regression Learner

Train regression models to predict data using supervised machine learning

Blöcke

RegressionEnsemble Predict

Predict responses using ensemble of decision trees for regression

Funktionen

alle erweitern

Boosted-Ensemble aus Bäumen

Erstellen von Regressions-Ensembles

`fitrensemble`	Fit ensemble of learners for regression
`compact`	Reduce size of machine learning model
`fitensemble`	Fit ensemble of learners for classification and regression

Modifizieren von Regressions-Ensembles

`regularize`	Find optimal weights for learners in regression ensemble
`removeLearners`	Remove members of compact regression ensemble
`resume`	Resume training of regression ensemble model
`shrink`	Prune regression ensemble

Kreuzvalidieren von Regressions-Ensembles

`cvshrink`	Cross-validate pruning and regularization of regression 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

Messen der Leistung

`loss`	Regression error for regression ensemble model
`resubLoss`	Resubstitution loss for regression ensemble model

Klassifizieren von Beobachtungen

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

Erfassen der Eigenschaften von Regressions-Ensembles

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

Bagged-Ensemble aus Bäumen

`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

Verbessern der Regression

crossval Cross-validate machine learning model

Auswerten der Regression

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`permutationImportance`	Predictor importance by permutation (Seit R2024a)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`predictorImportance`	Estimates of predictor importance for regression ensemble of decision trees
`shapley`	Shapley values

Multiantwort-Regression mit Regressionsketten

`fitrchains`	Multiresponse regression with regression chains (Seit R2024b)
`compact`	Reduce size of multiresponse regression model (Seit R2024b)
`loss`	Loss for multiresponse regression model (Seit R2024b)
`predict`	Predict responses using multiresponse regression model (Seit R2024b)

Vortrainiertes XGBoost-Modell von externen Plattformen

Importieren von XGBoost-Modellen

importModelFromXGBoost Import pretrained XGBoost model into MATLAB (Seit R2026a)

Messen der Leistung

loss Regression error for XGBoost model (Seit R2026a)

Auswerten von XGBoost

`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 XGBoost model (Seit R2026a)
`shapley`	Shapley values

Vorhersagen von Antworten

predict Predict responses using regression XGBoost model (Seit R2026a)

Erfassen von Eigenschaften von XGBoost

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

Objekte

alle erweitern

Boosted-Ensemble aus Bäumen

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

Bagged-Ensemble aus Bäumen

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

Multiantwort-Regression mit Regressionsketten

`RegressionChainEnsemble`	Multiresponse regression model (Seit R2024b)
`CompactRegressionChainEnsemble`	Compact multiresponse regression model (Seit R2024b)

Regressions-XGBoost

CompactRegressionXGBoost Compact regression XGBoost model (Seit R2026a)

Themen

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.
Working with Quantile Regression Models
Estimate prediction intervals and create models that are robust to outliers by using quantile regression models.
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.
Manually Perform Time Series Forecasting Using Ensembles of Boosted Regression Trees
Manually perform single-step and multiple-step time series forecasting with ensembles of boosted regression trees.