02563aam a2200253 4500008004500000020001800045082001600063100002600079245006900105260003000174300001600204440004100220520158900261650003601850650004201886650002301928650003201951700003801983700004002021700004202061942001202103999001902115952017502134170620b2015 xxu||||| |||| 00| 0 eng d a9781461471370 a519.5bJ2I6 aJames, Gareth9345009 aAn introduction to statistical learning: with applications in R bSpringer c2013aNew York axiv, 426 p. aSpringer texts in statistics9345010 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.
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aMathematical statistics9345011 aR - Computer program language9345012 aStatistics9345013 aMathematical models9345014 aHastie, TrevoreCo-author9345015 aWitten, Daniela eCo-author9345016 aTibshirani, RoberteCo-author9345017 2ddccBK c206552d206552 00102ddc406519_500000000000000_J2I6708NFIC9348005aVSLbVSLcGENd2017-06-21e8g3551.40l14m6o519.5 J2I6p194693q2020-10-27r2019-12-14s2019-12-14v4439.26yBK