04360nam 22006975 450 991073489190332120240522113232.097830313874703-031-38747-310.1007/978-3-031-38747-0(MiAaPQ)EBC30614337(Au-PeEL)EBL30614337(DE-He213)978-3-031-38747-0(PPN)272261017(CKB)27357707800041(EXLCZ)992735770780004120230630d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAn Introduction to Statistical Learning[electronic resource] with Applications in Python /by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (617 pages)Springer Texts in Statistics,2197-4136Print version: James, Gareth An Introduction to Statistical Learning Cham : Springer International Publishing AG,c2023 9783031387463 Introduction -- 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.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, 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.Springer Texts in Statistics,2197-4136StatisticsMathematical statisticsData processingStatistical Theory and MethodsStatistics and ComputingApplied StatisticsEstadística matemàticathubModels matemàticsthubPython (Llenguatge de programació)thubLlibres electrònicsthubStatistics.Mathematical statisticsData processing.Statistical Theory and Methods.Statistics and Computing.Applied Statistics.Estadística matemàticaModels matemàticsPython (Llenguatge de programació)519.5James Gareth849233Witten Daniela1372876Hastie Trevor102218Tibshirani Robert66318Taylor Jonathan1229526MiAaPQMiAaPQMiAaPQBOOK9910734891903321An Introduction to Statistical Learning3403792UNINA