Bagging

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In this class, we will introduce the bootstrap method and its application to learning a predictor called Bagging (Bootstrap AGGregatING). First we review bootstrap in statistics as a method to estimate the variance of an estimator on any statistic of a random variable (e.g. its mean). Then we extend this notion to machine learning, i.e., to learning a predictor for regression or classification. We discuss the pros and cons of bagging.

Notebook

References

An Introduction to the Bootstrap. B. Efron and R. Tibshirani, Chapman & Hall/CRC, (1993).

What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum. T. C. Hesterberg, The American Statistician, 69(4), 371–386, (2015).