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Bagging is the process of creating N learners on N different bootstrap samples, then taking the mean of their predictions.

My question is: Why not use any other type of sampling? Why use bootstrap samples?

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Interesting question. The bootstrap has good sampling properties, compared to some alternatives like the jackknife. The main downside of bootstrapping is that every iteration has to work with a sample that's as big as the original data set (which can be computationally expensive), while some other sampling techniques can work with much smaller samples.

This paper suggests that naïvely cutting the sample size can reduce performance, relative to bootstrap-based bagging, which would be a reason not to do so. The paper also introduces a novel method for using smaller samples in bagging estimates, while avoiding those problems.

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