03438cam a22004217a 4500999001700000001000600017010001700023020001800040020003300058040001000091072001300101082001000114100002500124245013400149246002500283260003400308260001100342300004600353490005200399500002000451520154300471526001202014650003302026650003102059650006202090650005802152650004002210650002102250700002702271700002602298700003002324830004002354856014602394856007402540942001702614952018502631952020002816 c28311d2831162947 a 2013936251 a9781461471370 a1461471370 (acid-free paper) aBTCTA 7aQA2lcco04a519.5 aJames, Gareth93607803aAn introduction to statistical learning :bwith applications in RcGareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani30aStatistical learning aNew York :bSpringer,cc2013. c©2013 axiv, 426 p. :bill. (some col.) ;c24 cm.1 aSpringer texts in statistics,x1431-875X ;v103 aIncludes index. 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.0 aCSCI323 0aMathematical statistics9834 0aMathematical models935121 0aMathematical statisticsvProblems, exercises, etc.913228 0aMathematical modelsvProblems, exercises, etc.936079 0aR (Computer program language)92458 0aStatistics920681 aWitten, Daniela9360801 aHastie, Trevor9360811 aTibshirani, Robert936082 0aSpringer texts in statistics936083 uhttps://static1.squarespace.com/static/5ff2adbe3fe4fe33db902812/t/6062a083acbfe82c7195b27d/1617076404560/ISLR%2BSeventh%2BPrinting.pdfzeBook uhttps://uowd.box.com/s/w3nr0q6amitab7ji3655n4lf5ia4tliczLocation Map cREGULAR2ddc 102ddc406519_500000000000000_JA_IN70937481aUOWDbUOWDcMAINd2016-01-19eAMAUKg40.49o519.5 JA INpT0053275r2017-01-26v40.49w2017-01-26yREGULARxAMAUK#203-9014869-0591526 102ddc406519_500000000000000_JA_IN70937482aUOWDbUOWDcMAINd2016-01-19eAMAUKg40.49l1o519.5 JA INpT0053276r2022-01-24s2021-10-06v40.49w2017-01-26yREGULARxAMAUK#203-9014869-0591526