LEADER 04601nam 22007335 450 001 9910495188803321 005 20240412130221.0 010 $a1-0716-1418-5 024 7 $a10.1007/978-1-0716-1418-1 035 $a(MiAaPQ)EBC6686746 035 $a(DE-He213)978-1-0716-1418-1 035 $a(PPN)269151834 035 $a(CKB)4100000011991160 035 $a(EXLCZ)994100000011991160 100 $a20210729d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn Introduction to Statistical Learning $ewith Applications in R /$fby Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani 205 $aSecond Edition. 210 1$aNew York, NY :$cSpringer US :$cImprint: Springer,$d2021. 215 $a1 online resource (xv, 607 p : il. col.) 225 1 $aSpringer Texts in Statistics,$x2197-4136 300 $aIncludes index. 311 1 $a1-0716-1417-7 311 1 $a9781071614174 327 $aPreface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index. 330 $aAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naīve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aArtificial intelligence 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aArtificial Intelligence 606 $aStatistics 606 $aEstadística matemātica$2thub 606 $aModels matemātics$2thub 606 $aR (Llenguatge de programaciķ)$2thub 608 $aLlibres electrōnics$2thub 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 0$aArtificial intelligence. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aArtificial Intelligence. 615 24$aStatistics. 615 7$aEstadística matemātica 615 7$aModels matemātics 615 7$aR (Llenguatge de programaciķ) 700 $aJames$b Gareth$g(Gareth Michael),$01251756 702 $aWitten$b Daniela 702 $aHastie$b Trevor 702 $aTibshirani$b Robert 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 801 2$bAzTeS 906 $aBOOK 912 $a9910495188803321 996 $aAn introduction to statistical learning$92901571 997 $aUNINA