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: | , , , |
|---|---|
| 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 |
目次:
- Introduction Statistical Learning Linear Regression Classification Resampling Methods Linear Model Selection and Regularization Moving Beyond Linearity Tree-Based Methods Support Vector Machines Unsupervised Learning Index.
- Introduction
- Statistical Learning
- Linear Regression
- Classification
- Resampling Methods
- Linear Model Selection and Regularization
- Moving Beyond Linearity
- Tree-Based Methods
- Support Vector Machines
- Unsupervised Learning
- Index