Insiemi di alberi di regressione

Foreste casuali, boosting e bagging degli alberi di regressione

Un insieme di alberi di regressione è un modello di previsione costituito da una combinazione ponderata di più alberi di regressione. In generale, la combinazione di più alberi di regressione migliora le prestazioni predittive. Per applicare il boosting agli alberi di regressione tramite LSBoost, utilizzare fitrensemble. Per applicare il bagging agli alberi di regressione o per creare una foresta casuale, utilizzare fitrensemble o TreeBagger. Per implementare la regressione quantile tramite il bagging degli alberi di regressione, utilizzare TreeBagger.

Per gli insiemi di classificazione, quali gli alberi di classificazione con boosting o bagging, gli insiemi di sottospazi casuali o i modelli a codici di output con correzione degli errori (ECOC) per la classificazione multiclasse, vedere Classification Ensembles (Insiemi di classificazione).

App

Regression Learner

Train regression models to predict data using supervised machine learning

Blocchi

RegressionEnsemble Predict

Predict responses using ensemble of decision trees for regression

Funzioni

espandi tutto

Insieme di alberi con boosting

Creazione di un insieme di regressione

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

Modifica dell'insieme di regressione

`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

Validazione incrociata dell'insieme di regressione

`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

Misurazione delle prestazioni

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

Classificazione delle osservazioni

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

Acquisizione delle proprietà dell'insieme di regressione

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

Insieme di alberi con bagging

`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

Miglioramento della regressione

crossval Cross-validate machine learning model

Interpretazione della regressione

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`permutationImportance`	Predictor importance by permutation (Da 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

Regressione multirisposta con catene di regressione

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

Modello XGBoost preaddestrato proveniente da piattaforme esterne

Importazione del modello XGBoost

importModelFromXGBoost Import pretrained XGBoost model into MATLAB (Da R2026a)

Misurazione delle prestazioni

loss Regression error for XGBoost model (Da R2026a)

Interpretazione di 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 (Da R2026a)
`shapley`	Shapley values

Previsione delle risposte

predict Predict responses using regression XGBoost model (Da R2026a)

Acquisizione delle proprietà di XGBoost

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

Oggetti

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Insieme di alberi con boosting

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

Insieme di alberi con bagging

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

Regressione multirisposta con catene di regressione

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

XGBoost per la regressione

CompactRegressionXGBoost Compact regression XGBoost model (Da R2026a)

Argomenti

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.