An Introduction to Statistical Learning : With Applications in R

Hastie, Trevor, Tibshirani, Robert, Witten, Daniela, James, Gareth

ISBN 10: 1461471370 / ISBN 13: 9781461471370

Edité par Springer New York, 2017

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Used book that is in excellent condition. May show signs of wear or have minor defects. N° de réf. du vendeur 14299651-6

Détails bibliographiques

Titre : An Introduction to Statistical Learning : ...

Éditeur : Springer New York

Date d'édition : 2017

Reliure : Couverture rigide

Etat : Very Good

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Synopsis :

An Introduction to Statistical Learning This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.

Présentation de l'éditeur:

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.

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.