The elements of statistical learning: data mining, inference, and prediction, 2nd Edition


In the words of the authors, the goal of this book was to " bring together many of the important new ideas in learning, and explain them in a statistical framework. " The authors have been quite successful in achieving this objective and their work will be a welcome addition to the statistics and learning literatures. Statistics has always been an interdisci-plinary, borrowing ideas from diverse fields and repaying the debt with contributions both theoretical and practical to the other intellectual disciplines. Within statistical learning this cross-fertilization is especially noticeable. Hastie, Tibshirani, and Friedman's book is a valuable resource both for the statistician needing an introduction to machine learning and related fields and for the computer scientist wishing to learn more about statistics. Statistician will especially appreciate that it is written in their own language. The level of the book is roughly that of a second-year PhD student in statistics and it will be useful as a textbook for such students. In a stimulating paper, Breiman (2001) argues that statistics has been focused much too on a " data modeling culture " where the model is paramount. Breiman argues instead for an " algorithmic modeling culture " with emphasis on black-box types of prediction. Breiman's paper is controversial and in his discussion Efron objects that " Prediction is certainly an interesting subject but Leo's paper overstates both its role and our profession's lack of interest in it. " Though I mostly agree with Efron, I have worried that the courses offered by most statistics departments include little if any treatment of statistical learning and prediction. (Stanford, where Efron as well as our authors teach, is an exception.) Graduate students in statistics certainly need to know more than they do now about prediction, machine learning, statistical learning, and data mining (not disjoint 1

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