Documentation

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 fitensemble 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.

Apps

Classification Learner Train models to classify data using supervised machine learning

Functions

fitensemble Fitted ensemble for classification or regression
templateEnsemble Ensemble learning template
predict Predict classification
oobPredict Predict out-of-bag response of ensemble
fitcecoc Fit multiclass models for support vector machines or other classifiers
templateSVM Support vector machine template
predict Predict labels for error-correcting output code multiclass classifiers
crossval Cross validate ensemble
loss Classification error
oobLoss Out-of-bag classification error
resume Resume training ensemble
predictorImportance Estimates of predictor importance
removeLearners Remove members of compact classification ensemble
compareHoldout Compare accuracies of two classification models using new data
loss Classification loss for error-correcting output code multiclass classifiers
crossval Cross-validated, error-correcting output code multiclass model
compact Compact error-correcting output codes multiclass model
designecoc Coding matrix for reducing error-correcting output code to binary
discardSupportVectors Discard support vectors of linear support vector machine binary learners
compareHoldout Compare accuracies of two classification models using new data

Classes

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

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