01841nam a22002657a 450000500170000000800410001702000180005804100080007608200250008410000190010924501060012826000380023430000170027250000200028952010550030965000280136465000240139265000280141665000240144465000300146865000150149870000200151370000190153370000230155220260314233858.0260314b        |||||||| |||| 00| 0 eng d  a9783031391897  aeng  a519.50285 T23bJAM W0 aJames, Gareth 13aAn introduction to statistical learning : bwith applications in  python / by cGareth James [et.al.]  aSwitzerland : bSpringer, c2023.  axvi, 426 p.   aIncludes index.  aAn 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 website0 aMathematical statistics0 aMathematical models0 aMathematical statistics0 aMathematical models0 aComputer program language0 aStatistics1 aWitten, Daniela1 aHastie, Trevor1 aTibshirani, Robert