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An Introduction to Statistical Learning: with Applications in R

  • Category: Computer
  • Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
  • Pages: 426 pages
  • File type: PDF (440 pages, 8.6 MB)

Read and download free eBook intituled An Introduction to Statistical Learning: with Applications in R in format PDF (440 pages, 8.6 MB) – 426 pages created by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.

This book 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.

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An Introduction to Statistical Learning: with Applications in R

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