000			03227nam a22003135i 4500
005			20210812160318.0
008			130625s2013 xxu| s |||| 0|eng d
020			_a9781461471387 _9978-1-4614-7138-7
050		4	_aQA276-280
082	0	4	_a519.5 _223
100	1		_aJames, Gareth. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	3	_aAn Introduction to Statistical Learning _h[electronic resource] : _bwith Applications in R / _cby Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
250			_a1st ed. 2013.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXIV, 426 p. 556 illus. _bonline resource.
490	1		_aSpringer Texts in Statistics, _x1431-875X ; _v103
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. 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.
650		0	_aStatistics .
650		0	_aArtificial intelligence.
650	1	4	_aStatistical Theory and Methods. _0https://scigraph.springernature.com/ontologies/product-market-codes/S11001
650	2	4	_aStatistics and Computing/Statistics Programs. _0https://scigraph.springernature.com/ontologies/product-market-codes/S12008
650	2	4	_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650	2	4	_aStatistics, general. _0https://scigraph.springernature.com/ontologies/product-market-codes/S0000X
700	1		_aWitten, Daniela. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aHastie, Trevor. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aTibshirani, Robert. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
856	4	0	_uhttps://doi.org/10.1007/978-1-4614-7138-7
999			_c1461 _d1461