Bagging predictors.¶
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Aggregation
- The aggregation schema recurs across domains, and methods often transfer cleanly even when tradeoffs must be rethought; ensemble averaging in machine learning, for example, was explicitly imported from the statistical aggregation tradition by Breiman (1996) when introducing bagging predictors.
This sourceIntroduces bootstrap aggregation (bagging) as a transfer of statistical aggregation into ensemble ML; directly supports the claim that ensemble averaging was explicitly imported from the statistical aggregation tradition.
Supported in partVerified against the publisher's abstract
Breiman's abstract presents bagging as aggregating multiple predictor versions, backing the ensemble-averaging half of the claim but not that he explicitly imported it from a statistical aggregation tradition.
“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 aggregation schema recurs across domains, and methods often transfer cleanly even when tradeoffs must be rethought; ensemble averaging in machine learning, for example, was explicitly imported from the statistical aggregation tradition by Breiman (1996) when introducing bagging predictors.
Verification¶
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Registry ID ref:361b51fd2fd8 · see in the full table