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@ -461,7 +461,7 @@ These modelling chapters are even more opinionated than the rest of the book. I
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techniques collectively known as statistical learning. For an even deeper
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understanding of the math behind the models, read the classic
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*Elements of Statistical Learning* by Trevor Hastie, Robert Tibshirani, and
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Jerome Friedman, <http://statweb.stanford.edu/~tibs/ElemStatLearn/> (also
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Jerome Friedman, <https://web.stanford.edu/~hastie/Papers/ESLII.pdf> (also
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available online for free).
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* *Applied Predictive Modeling* by Max Kuhn and Kjell Johnson,
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