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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage
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Springer Texts in Statistics
An Introduction
to Statistical
Learning
Gareth James
Daniela Witten
Trevor Hastie
Robert Tibshirani
with Applications in R
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 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
© Springer Science+Business Media New York 2013 (Corrected at 4 printing 2014)
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)
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