01959nam a22002897a 4500 20260314233858.0 260314b |||||||| |||| 00| 0 eng d 9783031391897 eng 519.50285 T23 JAM W James, Gareth 20153 An introduction to statistical learning : with applications in python / by Gareth James [et.al.] Switzerland : Springer, 2023. xvi, 426 p. Includes index. 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 Mathematical statistics 23078 Mathematical models 23712 Mathematical statistics 23078 Mathematical models 23712 Computer program language 23713 Statistics 23714 Witten, Daniela 20155 Hastie, Trevor 19595 Tibshirani, Robert 19597 BK 31561 31561 0 0 0 0 CLIB CLIB IITRPR 2026-03-14 2 519.50285 T23 JAM W 22817 2026-05-13 2026-04-13 21:49:56 2026-04-13 2026-03-14 BK