An introduction to statistical learning : with applications in R
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. Thi...
保存先:
| 主要な著者: | , , , |
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| フォーマット: | Livre numérique |
| 言語: | Anglais |
| 出版事項: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| 版: | 1st ed. 2013. |
| シリーズ: | Springer Texts in Statistics
103 |
| 主題: | |
| オンライン･アクセス: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 注記: |
Autre auteur : Robert Tibshirani Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • An introduction to statistical learning, with applications in R, Gareth James, Daniela Witten, Trevor Hastie... [et autre], 2013, New York, Springer, 1 volume (XIV-426 pages), Springer texts in statistics, 978-1-4614-7137-0, Texte imprimé • An Introduction to Statistical Learning, Texte imprimé, 9781461471370 • An Introduction to Statistical Learning, Texte imprimé, 9781461471394 • An Introduction to Statistical Learning, Texte imprimé, 9781071613054 |
| 要約: | 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 |
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| 記述事項: | Autre auteur : Robert Tibshirani Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| 書誌: | Index |
| ISBN: | 9781461471387 |
| ISSN: | 2197-4136 |
| アクセス: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |