An Introduction to Statistical Learning with Applications in R (Springer Texts in Statistics) Gareth James | 1461471370

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An Introduction to Statistical Learning provides an approachable summary of the field of statistical learning, an necessary toolset for creating 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. It offers some of the most important modeling and prediction methods, along with related applications. Topics cover linear regression, classification, resampling techniques, shrinkage approaches, tree-based methods, help vector machines, clustering, and more. Color graphics and real-world cases are used to explain the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning methods by practitioners in science, industry, and other areas, 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 Components 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 incorporates many of the related 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 methods to interpret their data. The text assumes only a previous course in linear regression and no understanding of matrix algebra.
  • ISBN-13: 9781461471370
  • ISBN-10: 1461471370
  • Language- English
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