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An Introduction to Statistical Learning

with Applications in R

  • Gareth James
  • Daniela Witten
  • Trevor Hastie
  • Robert Tibshirani

Part of the Springer Texts in Statistics book series (STS, volume 103)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 1-14
  3. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 15-57
  4. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 59-126
  5. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 127-173
  6. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 175-201
  7. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 203-264
  8. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 265-301
  9. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 303-335
  10. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 337-372
  11. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
    Pages 373-418
  12. Back Matter
    Pages 419-426

About this book

Introduction

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 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 science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The 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. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Keywords

R R software data mining inference statistical learning supervised learning unsupervised learning

Authors and affiliations

  • Gareth James
    • 1
  • Daniela Witten
    • 2
  • Trevor Hastie
    • 3
  • Robert Tibshirani
    • 3
  1. 1.Department of Data Sciences and OperationsUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  3. 3.Department of StatisticsStanford UniversityStanfordUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-7138-7
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-7137-0
  • Online ISBN 978-1-4614-7138-7
  • Series Print ISSN 1431-875X
  • Series Online ISSN 2197-4136
  • Buy this book on publisher's site