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An introduction to statistical learning provides an accessible overview of the field of statistical learning, an essential tool to capture the extensive and complex datasets that have emerged over the past 20 years in the fields of biology, finance, marketing and astrophysics. This book introduces some of the most important modeling and forecasting techniques as well as relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and practical examples illustrate the methods presented. Since the purpose of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter provides guidance on how to implement the analysis and analysis presented in R, a hugely popular open source statistical software platform Methods.
Two of the authors co-wrote the elements of statistical learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference work for statistics and machine learning researchers. An introduction to statistical learning covers many of the same topics, but at a level that is accessible to a much wider audience. This book is intended for statisticians and non-statisticians alike who want to use the most up-to-date statistical learning techniques to analyze their data. The text assumes only a previous course of the linear regression and no knowledge of the matrix algebra.