| Titre : | An introduction to statistical learning : With applications in R | | Type de document : | texte imprimé | | Auteurs : | Gareth James, Auteur ; Daniela Witten, Auteur ; Trevor Hastie, Auteur ; Robert Tibshinari, Auteur | | Editeur : | Springer | | Année de publication : | 2013 | | Importance : | 426 p. | | Présentation : | graph., tabl., index. | | ISBN/ISSN/EAN : | 978-1-4614-7137-0 | | Langues : | Anglais (eng) | | Catégories : | Liste Plan de classement
F104 (Probabilités - Statistiques) [Classement Kourou]
| | Résumé : | An introduction to Statistical Learning provides an accesible 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 predition techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based examples are used to illustrate the methods presented. Since the goal of this texbook is to facilitade to 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. | | Type de document : | Livre | | Permalien de la notice : | https://infodoc.agroparistech.fr/index.php?lvl=notice_display&id=170331 |
An introduction to statistical learning : With applications in R [texte imprimé] / Gareth James, Auteur ; Daniela Witten, Auteur ; Trevor Hastie, Auteur ; Robert Tibshinari, Auteur . - [S.l.] : Springer, 2013 . - 426 p. : graph., tabl., index. ISBN : 978-1-4614-7137-0 Langues : Anglais ( eng) | Catégories : | Liste Plan de classement
F104 (Probabilités - Statistiques) [Classement Kourou]
| | Résumé : | An introduction to Statistical Learning provides an accesible 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 predition techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based examples are used to illustrate the methods presented. Since the goal of this texbook is to facilitade to 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. | | Type de document : | Livre | | Permalien de la notice : | https://infodoc.agroparistech.fr/index.php?lvl=notice_display&id=170331 |
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