Titlebook: Statistical Learning from a Regression Perspective; Richard A. Berk Textbook 20162nd edition Springer Nature Switzerland AG 2016 classific

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Bagging,nts, measures of it, residuals, classifications and others. Ensemble methods make many passes through the data after which the results are combined. One immediate benefit can be estimates with better out-of-sample performance. The statistical learning procedure called “bagging” is where we start.

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Boosting,ore flexible. A much wider range of response variables types are can be used, all within the same basic algorithmic structure. Just as for random forests, there are several useful ways to study the output, and excellent software exists within R.

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978-3-319-82969-2Springer Nature Switzerland AG 2016

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Statistical Learning from a Regression Perspective978-3-319-44048-4Series ISSN 1431-875X Series E-ISSN 2197-4136

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Random Forests,In the last chapter some of the weaknesses of bagging were discussed. In this chapter, random forests is introduced in part to address these weaknesses. Random forests is a legitimate and very useful statistical learning procedure that can be successfully used in practice.

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