000			01959nam a22002897a 4500
005			20260314233858.0
008			260314b |||||||| |||| 00| 0 eng d
020			_a9783031391897
041			_aeng
082			_a519.50285 T23 _bJAM W
100	0		_aJames, Gareth _920153
245	1	3	_aAn introduction to statistical learning : _bwith applications in python / by _cGareth James [et.al.]
260			_aSwitzerland : _bSpringer, _c2023.
300			_axvi, 426 p.
500			_aIncludes index.
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. - Taken from publisher's website
650	0		_aMathematical statistics _923078
650	0		_aMathematical models _923712
650	0		_aMathematical statistics _923078
650	0		_aMathematical models _923712
650	0		_aComputer program language _923713
650	0		_aStatistics _923714
700	1		_aWitten, Daniela _920155
700	1		_aHastie, Trevor _919595
700	1		_aTibshirani, Robert _919597
942			_cBK
999			_c31561 _d31561