An with applications in python / by James, Gareth creator Witten, Daniela Hastie, Trevor Tibshirani, Robert text Switzerland Springer 2023 monographic eng

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xvi, 426 p. 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, deep learning, survival analysis, multiple testing, 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. - Taken from publisher's website Gareth James [et.al.] Includes index. Mathematical statistics Mathematical models Mathematical statistics Mathematical models Computer program language Statistics 519.50285 T23 JAM W 9783031391897 260314 20260314233858.0