Bagging predictors. Breiman, L. Machine Learning, 24(2):123-140, Springer Nature, 10, 2004. ![Bagging predictors [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Bagging predictors is a method for generating multiple versions of a pre-dictor and using these to get an aggregated predictor. The aggregation av-erages over the versions when predicting a numerical outcome and does a plurality v ote 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 classiication and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability o f the prediction method. If perturbing the learning set can cause signiicant changes in the predictor constructed, then bagging can improve accuracy.
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abstract = {Bagging predictors is a method for generating multiple versions of a pre-dictor and using these to get an aggregated predictor. The aggregation av-erages over the versions when predicting a numerical outcome and does a plurality v ote 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 classiication and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability o f the prediction method. If perturbing the learning set can cause signiicant changes in the predictor constructed, then bagging can improve accuracy.},
bibtype = {article},
author = {Breiman, Leo},
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