Book Reviews 567 The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

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 is a welcome addition to the statistics and learning literatures. Statistics has always been interdisciplinary, borrowing ideas from diverse elds and repaying the debt with contributions, both theoretical and practical, to the other intellectual disciplines. For statistical learning, this cross-fertilization is especially noticeable. This book is a valuable resource, both for the statisti-cian needing an introduction to machine learning and related elds and for the computer scientist wishing to learn more about statistics. Statisticians will es-pecially appreciate that it is written in their own language. The level of the book is roughly that of a second-year doctoral student in sta-tistics, and it will be useful as a textbook for such students. In a stimulating arti-cle, Breiman (2001) argued that statistics has been focused too much on a “data modeling culture, ” where the model is paramount. Breiman argued instead fo