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Titre : An introduction to statistical learning : with applications in R Type de document : Guide/Manuel Auteurs : Gareth James, Auteur ; Daniela Witten, Auteur ; Trevor Hastie, Auteur ; Robert Tibshirani, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2013 Collection : Springer texts in statistics, ISSN 1431-875X num. 103 Présentation : 426 p. ISBN/ISSN/EAN : 978-1-4614-7138-7 Langues : Anglais (eng) Résumé : (Editeur) [Introduction] 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. Note de contenu : Numéro de notice : 26292 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Manuel informatique DOI : 10.1007/978-1-4614-7138-7 En ligne : https://doi.org/10.1007/978-1-4614-7138-7 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94961
