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04601aam a2200649 i 4500 001 CDE25B28214711EEBC7340321FECA4DB 003 SILO 005 20230713010558 008 160120t20152013nyua 001 0 eng c 020 $a 1461471389 020 $a 9781461471387 020 $a 1461471370 020 $a 9781461471370 035 $a (OCoLC)935355844 040 $a OCC $b eng $e rda $c OCC $d OSU $d OCLCF $d USU $d TSC $d DHA $d OCLCQ $d QGQ $d OCLCA $d CBU $d OCLCO $d OCLCA $d PAU $d OCLCO $d LHT $d OCLCQ $d OCLCO $d SILO 042 $a pcc 050 4 $a QA276 $b .I58 2015 060 4 $a QA276 $b .I58 2015 082 0 $a 519.5 $2 23 084 $a 85.03 $2 bcl 245 03 $a An introduction to statistical learning : $b with applications in R / $c Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. 246 30 $a Statistical learning 250 $a [Corrected at 6th printing 2015]. 264 1 $a New York : $b Springer : $c 2015. 300 $a xiv, 426 pages : $b illustrations (chiefly color) ; $c 25 cm 490 1 $a Springer texts in statistics, $x 1431-875X ; $v 103 500 $a Includes index. 505 0 $a Introduction -- Statistical learning -- Linear regression -- Classification -- Resampling methods -- Linear model selection and regularization -- Moving beyond linearity -- Tree-based methods -- Support vector machines -- Unsupervised learning. 520 $a "An 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. Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields. Analyses and methods are presented in R. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"--Publisher description. 650 0 $a Mathematical statistics. 650 0 $a Mathematical models. 650 0 $a Mathematical statistics $v Problems, exercises, etc. 650 0 $a Mathematical models $v Problems, exercises, etc. 650 0 $a R (Computer program language) 650 0 $a Statistics. 650 12 $a Models, Statistical 650 12 $a Statistics as Topic 650 2 $a Models, Theoretical 650 6 $a ModeÌles matheÌmatiques. 650 6 $a ModeÌles matheÌmatiques $v ProbleÌmes et exercices. 650 6 $a R (Langage de programmation) 650 6 $a Statistiques. 650 7 $a mathematical models. $2 aat 650 7 $a Mathematical models. $2 fast $0 (OCoLC)fst01012085 650 7 $a Mathematical statistics. $2 fast $0 (OCoLC)fst01012127 650 7 $a R (Computer program language) $2 fast $0 (OCoLC)fst01086207 650 7 $a Statistics. $2 fast $0 (OCoLC)fst01132103 655 7 $a Problems and exercises. $2 fast $0 (OCoLC)fst01423783 700 1 $a James, Gareth $q (Gareth Michael), $e author. 700 1 $a Witten, Daniela, $e author. 700 1 $a Hastie, Trevor, $e author. 700 1 $a Tibshirani, Robert, $e author. 830 0 $a Springer texts in statistics. 941 $a 1 952 $l PLAX964 $d 20230718092630.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=CDE25B28214711EEBC7340321FECA4DB 994 $a 92 $b IOHInitiate Another SILO Locator Search