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Springer Texts in Statistics Gareth James Daniela Witten Trevor Hastie Robert Tibshirani An Introduction to Statistical Learning with Applications in R Springer Texts in Statistics Gareth James · Daniela Witten · Trevor Hastie · Robert Tibshirani An Introduction to Statistical Learning with Applications in R An Introduction to Statistical Learning provides an accessible overview of the ﬁ eld of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in ﬁ elds ranging from biology to ﬁ nance to marketing to astrophysics in the past twenty years. Th is book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classiﬁ cation, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in sci- ence, industry, and other ﬁ elds, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical soft ware platform. Two of the authors co-wrote Th e Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Th is book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learn- ing techniques to analyze their data. Th e text assumes only a previous course in linear regression and no knowledge of matrix algebra. Gareth James is a professor of statistics at University of Southern California. He has published an extensive body of methodological work in the domain of statistical learn- ing with particular emphasis on high-dimensional and functional data. Th e conceptual framework for this book grew out of his MBA elective courses in this area. Daniela Witten is an assistant professor of biostatistics at University of Washington. Her research focuses largely on high-dimensional statistical machine learning. She has contributed to the translation of statistical learning techniques to the ﬁ eld of genomics, through collaborations and as a member of the Institute of Medicine committee that led to the report Evolution of Translational Omics. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling soft ware and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Statistics I SBN 978- 1- 4614- 7137- 0 9 781461 471370 STS J a m e s · W i t t e n · H a s t i e · T i b s h i r a n i 1 A n I n t r o d u c t i o n t o S t a t i s t i c a l L e a r n n g i

Springer Texts in Statistics 103 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 Department of Information and Operations Management 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 DOI 10.1007/978-1-4614-7138-7 Springer New York Heidelberg Dordrecht London ISBN 978-1-4614-7138-7 (eBook) Library of Congress Control Number: 2013936251 © Springer Science+Business Media New York 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissim- ilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied speciﬁcally for the pur- pose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publi- cation does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

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 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- dents in statistics or related quantitative ﬁelds or for individuals in other vii

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