The Elements of Statistical Learning Data Mining, Inference, and Pre
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has le
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Springer Series in Statistics
For other titles published in this series, go to http://www.springer.com/series/692
Trevor Hastie Robert Tibshirani Jerome Friedman
The Elements of Statistical Learning Data Mining, Inference, and Prediction Second Edition
Trevor Hastie Stanford University Dept. of Statistics Stanford CA 94305 USA [email protected]
Robert Tibshirani Stanford University Dept. of Statistics Stanford CA 94305 USA [email protected]
Jerome Friedman Stanford University Dept. of Statistics Stanford CA 94305 USA [email protected]
ISSN: 0172-7397 ISBN: 978-0-387-84857-0 DOI: 10.1007/b94608
e-ISBN: 978-0-387-84858-7
Library of Congress Control Number: 2008941148 c Springer Science+Business Media, LLC 2009, Corrected at 12th printing 2017 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com
To our parents: Valerie and Patrick Hastie Vera and Sami Tibshirani Florence and Harry Friedman
and to our families: Samantha, Timothy, and Lynda Charlie, Ryan, Julie, and Cheryl Melanie, Dora, Monika, and Ildiko
Preface to the Second Edition
In God we trust, all others bring data.
–William Edwards Deming (1900-1993)1
We have been gratified by the popularity of the first edition of The Elements of Statistical Learning. This, along with the fast pace of research in the statistical learning field, motivated us to update our book with a second edition. We have added four new chapters and updated some of the existing chapters. Because many readers are familiar with the layout of the first edition, we have tried to change it as little as possible. Here is a summary of the main changes: 1 On
the Web, this quote has been widely attributed to both Deming and Robert W. Hayden; however Professor Hayden told us that he can claim no credit for this quote, and ironically we could find no “data” confirming that Deming actually said this.
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Preface to the Second Edition
Chapter 1. Introduction 2. Overview of Supervised Learning 3. Linear Methods for Regression 4. Linear Methods for Classification 5. Basis Expansions and Regularization 6. Kernel Smoothing Methods 7. Model Assessment and Selection 8. Model Inference and Averaging 9. Additive Models, Trees, and Related Methods 10. Boosting and Additive Trees 11. Neural Networks 12. Support Vector Machines and Flexible Discriminants 13. Prototype Methods and Nearest-Neig
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