**A partir de cette page vous pouvez :**

Retourner au premier Ã©cran avec les catÃ©gories... |

### DÃ©tail de l'auteur

### Auteur James, Gareth (Gareth Michael)

### Documents disponibles Ã©crits par cet auteur

Affiner la rechercheAn introduction to statistical learning / James, Gareth (Gareth Michael)

Titre : An introduction to statistical learning : with applications in R Type de document : texte imprimÃ© Auteurs : James, Gareth (Gareth Michael), Auteur; Trevor Hastie, Auteur; Witten, Daniela, Auteur Mention d'Ã©dition : Second edition Editeur : New York : Springer AnnÃ©e de publication : 2021 Importance : XV, 607p PrÃ©sentation : illustrations (some color)., Format : 23 ISBN/ISSN/EAN : 978-1-07-161417-4 Langues : Anglais ( eng)CatÃ©gories : 540 Chimie et sciences connexes:500 Sciences de la nature et mathÃ©matiques:519 ProbabilitÃ©s et mathÃ©matiques appliquÃ©es:519.5 Statistique mathÃ©matique Mots-clÃ©s : An introduction to statistical learning Index. dÃ©cimale : 519.5 RÃ©sumÃ© : 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, 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 naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility

An introduction to statistical learning [texte imprimÃ©] : with applications in R / James, Gareth (Gareth Michael), Auteur; Trevor Hastie, Auteur; Witten, Daniela, Auteur . - Second edition . - New York : Springer, 2021 . - XV, 607p : illustrations (some color)., ; 23.ISBN: 978-1-07-161417-4

Langues : Anglais (eng)

CatÃ©gories : 540 Chimie et sciences connexes:500 Sciences de la nature et mathÃ©matiques:519 ProbabilitÃ©s et mathÃ©matiques appliquÃ©es:519.5 Statistique mathÃ©matique Mots-clÃ©s : An introduction to statistical learning Index. dÃ©cimale : 519.5 RÃ©sumÃ© : 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, 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 naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility

## Exemplaires

Code-barres Cote Support Localisation Section DisponibilitÃ© 125604 519.5 JAM Livre Library Repository Exclu du prÃªt