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They are easy to
fit, easy to use, and easy to interpret as
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a fixed sequence
of simple tests. (Doctors like them.)
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They are
non-linear, so they work much better than
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linear models for
highly non-linear functions.
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They typically
generalize less well than non-linear
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models that use
adaptive basis functions, but its easy to
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improve them by
averaging the predictions of many trees
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Each
tree is fitted to a training set produced by
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sampling
the dataset with replacement (“Bagging”)
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So
much for interpretability!
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