Documentation

Bagging

Improve predictions using bootstrap aggregation

To train a ensemble of bagged classification trees, or to explore other ensemble-learning options, use the Classification Learner App.

For greater flexibility, train a bagged ensemble of decision trees using fitensemble. To train a multiclass model of bagged classification trees, create an ensemble template using templateEnsemble, and then pass it and the training data to fitcecoc.

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
predict Predict response of ensemble
oobPredict Predict out-of-bag response of ensemble
loss Classification error
crossval Cross validate ensemble
predictorImportance Estimates of predictor importance
resume Resume training ensemble
loss Regression error
crossval Cross validate ensemble
cvshrink Cross validate shrinking (pruning) ensemble
predictorImportance Estimates of predictor importance
resume Resume training ensemble

Classes

ClassificationBaggedEnsemble Classification ensemble grown by resampling
RegressionBaggedEnsemble Regression ensemble grown by resampling
CompactClassificationEnsemble Compact classification ensemble class
ClassificationPartitionedEnsemble Cross-validated classification ensemble
CompactRegressionEnsemble Compact regression ensemble class
RegressionPartitionedEnsemble Cross-validated regression ensemble
CompactTreeBagger Compact ensemble of decision trees grown by bootstrap aggregation
TreeBagger Names of classes

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