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Classification Ensembles

Boosting, random forest, bagging, random subspace, and ECOC ensembles for multiclass learning

A classification ensemble is a predictive model composed of a weighted combination of multiple classification models. In general, combining multiple classification models increases predictive performance.

To explore classification ensembles interactively, use the Classification Learner app. For greater flexibility, use fitcensemble in the command-line interface to boost or bag classification trees, or to grow a random forest [11]. For details on all supported ensembles, see Ensemble Algorithms. To reduce a multiclass problem into an ensemble of binary classification problems, train an error-correcting output codes (ECOC) model. For details, see fitcecoc.

To boost regression trees using LSBoost, or to grow a random forest of regression trees[11], see Regression Ensembles.


Classification Learner Train models to classify data using supervised machine learning


fitcensemble Fit ensemble of learners for classification
predict Predict labels using ensemble of classification models
oobPredict Predict out-of-bag response of ensemble
templateEnsemble Ensemble learning template
fitcensemble Fit ensemble of learners for classification
predict Predict responses using ensemble of bagged decision trees
oobPredict Ensemble predictions for out-of-bag observations
fitcecoc Fit multiclass models for support vector machines or other classifiers
templateSVM Support vector machine template
predict Predict labels using error-correcting output code multiclass classification model


ClassificationEnsemble Ensemble classifier
CompactClassificationEnsemble Compact classification ensemble class
ClassificationPartitionedEnsemble Cross-validated classification ensemble
TreeBagger Names of classes
CompactTreeBagger Compact ensemble of decision trees grown by bootstrap aggregation
ClassificationBaggedEnsemble Classification ensemble grown by resampling
ClassificationECOC Multiclass model for support vector machines or other classifiers
CompactClassificationECOC Compact multiclass model for support vector machines or other classifiers
ClassificationPartitionedECOC Cross-validated multiclass model for support vector machines or other classifiers

Examples and How To

Train Ensemble Classifiers Using Classification Learner App

Learn how to train ensembles of classifiers.

Framework for Ensemble Learning

Construct ensembles of classifiers in the Classification Learner app.

Basic Ensemble Examples

Create a classification tree ensemble for the ionosphere data set, and use it to predict the classification of a radar return with average measurements.

Test Ensemble Quality

Learn the methods to obtain a better idea of the quality of an ensemble.

Classification: Imbalanced Data or Unequal Misclassification Costs

Learn to set prior class probabilities or misclassification costs.

Classification with Many Categorical Levels

Train an ensemble of classification trees using data containing predictors with many categorical levels.

Ensemble Regularization

Automatically choose fewer weak learners for an ensemble in a way that does not diminish predictive performance.

Tune RobustBoost

Tune RobustBoost parameters for better predictive accuracy. (RobustBoost requires Optimization Toolbox.)

Surrogate Splits

Gain better predictions when you have missing data using surrogate splits.

TreeBagger Examples

Run TreeBagger on sample data.

Parallel Treebagger

Improve performance by running TreeBagger in parallel.

Random Subspace Classification

Learn how to use a random subspace ensemble to increase the accuracy of classification.


Ensemble Algorithms

A number of algorithms are available for ensemble learning.

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