LEADER 04360nam 22006975 450 001 9910734891903321 005 20240522113232.0 010 $a9783031387470 010 $a3-031-38747-3 024 7 $a10.1007/978-3-031-38747-0 035 $a(MiAaPQ)EBC30614337 035 $a(Au-PeEL)EBL30614337 035 $a(DE-He213)978-3-031-38747-0 035 $a(PPN)272261017 035 $a(CKB)27357707800041 035 $a(EXLCZ)9927357707800041 100 $a20230630d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn Introduction to Statistical Learning$b[electronic resource] $ewith Applications in Python /$fby Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (617 pages) 225 1 $aSpringer Texts in Statistics,$x2197-4136 311 08$aPrint version: James, Gareth An Introduction to Statistical Learning Cham : Springer International Publishing AG,c2023 9783031387463 327 $aIntroduction -- Statistical Learning -- Linear Regression -- Classification -- Resampling Methods -- Linear Model Selection and Regularization -- Moving Beyond Linearity -- Tree-Based Methods -- Support Vector Machines -- Deep Learning -- Survival Analysis and Censored data -- Unsupervised Learning -- Multiple Testing -- Index. 330 $aAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aApplied Statistics 606 $aEstadística matemātica$2thub 606 $aModels matemātics$2thub 606 $aPython (Llenguatge de programaciķ)$2thub 608 $aLlibres electrōnics$2thub 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aApplied Statistics. 615 7$aEstadística matemātica 615 7$aModels matemātics 615 7$aPython (Llenguatge de programaciķ) 676 $a519.5 700 $aJames$b Gareth$0849233 701 $aWitten$b Daniela$01372876 701 $aHastie$b Trevor$0102218 701 $aTibshirani$b Robert$066318 701 $aTaylor$b Jonathan$01229526 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734891903321 996 $aAn Introduction to Statistical Learning$93403792 997 $aUNINA