A regression tree ensemble is a predictive model composed of a weighted combination of multiple regression trees. In general, combining multiple regression trees increases predictive performance. To boost regression trees using LSBoost, use fitrensemble
. To bag regression trees or to grow a random forest [12], use fitrensemble
or TreeBagger
. To implement quantile regression using a bag of regression trees, use TreeBagger
.
For classification ensembles, such as boosted or bagged classification trees, random subspace ensembles, or error-correcting output codes (ECOC) models for multiclass classification, see Classification Ensembles.
Regression Learner | Train regression models to predict data using supervised machine learning |
RegressionEnsemble Predict | Predict responses using ensemble of decision trees for regression |
Learn about different algorithms for ensemble learning.
Framework for Ensemble Learning
Obtain highly accurate predictions by using many weak learners.
Train a simple regression ensemble.
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.
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.