Context

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Learned in this study

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Things to explore

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Overview

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• Given a training set $D$ of size $n$, bagging generates $m$ new training sets $D_i$, each of size $n’$, by sampling from $D$ uniformly and with replacement (the same sample may be present multiple times)
• The $m$ models are fitted using the $m$ bootstrap samples and combined by averaging the output (for regression) or voting (for classification)

Notes

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• Bagging (bootstrap aggregating) can push a good but unstable procedure a significant step towards optimality
• On the other hand, it can slightly degrade the performance of stable procedures

See also

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References

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• Breiman, Leo. “Bagging predictors.” Machine learning 24.2 (1996): 123-140.
• https://en.wikipedia.org/wiki/Bootstrap_aggregating