000			01764nam a22002297a 4500
005			20241211163246.0
008			241211b |||||||| |||| 00| 0 eng d
020			_a9781071614204
040			_cNational Institute of Technology Goa
082			_a519.5 _bJAM/INT
100			_aJames, Gareth
110			_aWitten, Daniela
111			_aHastie, Trevor
245			_aAn introduction to statistical learning: with applications in R
250			_a2nd
260			_aNew York: _b Springer, _c 2021
300			_axx, 608p.: 6x11x2; Paperback
520			_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.
650			_2Mathematics _aMathematics; R Computer program language; Mathematical statistics; Linear regression; Resampling methods
700			_aTibshirani, Robert
942			_2ddc _cBK _n0
999			_c5158 _d5158