Bagging predictors

@article{Breiman1996BaggingP,
  title={Bagging predictors},
  author={L. Breiman},
  journal={Machine Learning},
  year={1996},
  volume={24},
  pages={123-140}
}
  • L. Breiman
  • Published 1996
  • Computer Science
  • Machine Learning
Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear… Expand
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