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Springer Texts in Statistics

Series Editors: G. Casella S. Fienberg I. Olkin

For further volumes: http://www.springer.com/series/417

Gareth James • Daniela Witten • Trevor Hastie Robert Tibshirani

An Introduction to Statistical Learning

with Applications in R

123

Gareth James

Operations University of Southern California Los Angeles, CA, USA

Trevor Hastie Department of Statistics Stanford University Stanford, CA, USA

Daniela Witten Department of Biostatistics University of Washington Seattle, WA, USA

Robert Tibshirani Department of Statistics Stanford University Stanford, CA, USA

ISSN 1431-875X ISBN 978-1-4614-7137-0 ISBN 978-1-4614-7138-7 (eBook) DOI 10.1007/978-1-4614-7138-7 Springer New York Heidelberg Dordrecht London

Library of Congress Control Number: 2013936251

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Department of Data Sciences and

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To our parents:

Alison and Michael James

Chiara Nappi and Edward Witten

Valerie and Patrick Hastie

Vera and Sami Tibshirani

and to our families:

Michael, Daniel, and Catherine

Tessa, Theo, and Ari

Samantha, Timothy, and Lynda

Charlie, Ryan, Julie, and Cheryl

Preface

Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. With the explosion of “Big Data” problems, statistical learning has be-

come a very hot field in many scientific areas as well as marketing, finance, and other business disciplines. People with statistical learning skills are in high demand. One of the first books in this area—The Elements of Statistical Learning

(ESL) (Hastie, Tibshirani, and Friedman)—was published in 2001, with a second edition in 2009. ESL has become a popular text not only in statis- tics but also in related fields. One of the reasons for ESL’s popularity is its relatively accessible style. But ESL is intended for individuals with ad- vanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less tech- nical treatment of these topics. In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details. We have created labs illus- trating how to implement each of the statistical learning methods using the popular statistical software package R. These labs provide the reader with valuable hands-on experience. This book is appropriate for advanced undergraduates or master’s stu-