Regression Tree Ensembles
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.
Apps
Regression Learner | Train regression models to predict data using supervised machine learning |
Blocks
RegressionEnsemble Predict | Predict responses using ensemble of decision trees for regression (Since R2021a) |
Functions
Classes
Topics
- 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.
- 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.