Difference between revisions of "Bagging"
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== Summary == | == Summary == | ||
| − | Bagging (a.k.a | + | Bagging (a.k.a '''b'''ootstrap '''agg'''regat'''ing''') is an ensemble machine learning [[category::method]] for classification and regression, which generates multiple versions of a predictor, by making bootstrap replications of the learning set and using them as the new learning set, and uses them to produce an aggregated predictor, which does voting over the different versions for classification and averages outcomes when predicting numerical values. |
== Method == | == Method == | ||
Revision as of 17:53, 30 November 2010
Summary
Bagging (a.k.a bootstrap aggregating) is an ensemble machine learning method for classification and regression, which generates multiple versions of a predictor, by making bootstrap replications of the learning set and using them as the new learning set, and uses them to produce an aggregated predictor, which does voting over the different versions for classification and averages outcomes when predicting numerical values.
Method
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References / Links
- Leo Brieman. Bagging Predictors. Machine Learning, 24, 123–140 (1996).
- Wikipedia article on bagging.
