BAG.h File Reference

This class implements the bootstrap aggregator classifier. Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of other machine learning algorithms. Bagging also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, the BAG class can be used with any type of GRT classifier. Bagging is a special case of the model averaging. More...

#include "../../CoreModules/Classifier.h"

Go to the source code of this file.

Classes
class	BAG

Detailed Description

This class implements the bootstrap aggregator classifier. Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of other machine learning algorithms. Bagging also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, the BAG class can be used with any type of GRT classifier. Bagging is a special case of the model averaging.

Author

Nicholas Gillian ngill.nosp@m.ian@.nosp@m.media.nosp@m..mit.nosp@m..edu

Version

1.0

Remarks

This implementation is based on Breiman, Leo. "Bagging predictors." Machine learning 24, no. 2 (1996): 123-140.

Definition in file BAG.h.