000			03029cam a22003977a 4500
999			_c28311 _d28311
010			_a 2013936251
020			_a9781461471370
020			_a1461471370 (acid-free paper)
072		7	_aQA _2lcco
082	0	4	_a519.5
100			_aJames, Gareth _936078
245	0	3	_aAn introduction to statistical learning : _bwith applications in R _cGareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
246	3	0	_aStatistical learning
260			_aNew York : _bSpringer, _cc2013.
260			_c©2013
300			_axiv, 426 p. : _bill. (some col.) ; _c24 cm.
490	1		_aSpringer texts in statistics, _x1431-875X ; _v103
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, 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.</p> <p>Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
526	0		_aCSCI323
650		0	_aMathematical statistics _9834
650		0	_aMathematical models _935121
650		0	_aMathematical statistics _vProblems, exercises, etc. _913228
650		0	_aMathematical models _vProblems, exercises, etc. _936079
650		0	_aR (Computer program language) _92458
650		0	_aStatistics _92068
700	1		_aWitten, Daniela _936080
700	1		_aHastie, Trevor _936081
700	1		_aTibshirani, Robert _936082
830		0	_aSpringer texts in statistics _936083
856			_uhttps://static1.squarespace.com/static/5ff2adbe3fe4fe33db902812/t/6062a083acbfe82c7195b27d/1617076404560/ISLR%2BSeventh%2BPrinting.pdf _zeBook
856			_uhttps://uowd.box.com/s/w3nr0q6amitab7ji3655n4lf5ia4tlic _zLocation Map