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 up to now twenty years. This book presents one of the vital most important modeling and prediction techniques, together with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, fortify 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 using these statistical learning techniques by practitioners in science, industry, and other fields, each and every 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 some of the same topics, but at a level accessible to a wider audience. This book is targeted at statisticians and non-statisticians alike who want to use state of the art statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
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