An introduction to statistical learning : with applications in R / Gareth James... [et al.].
Contributor(s): James, Gareth
| Witten, Daniela
| Hastie, Trevor
| Tibshirani, Robert
.
Material type: 






Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
![]() |
EWU Library E-book | Non-fiction | 519.5 INT 2017 (Browse shelf) | Not for loan | ||||
![]() |
EWU Library Reserve Section | Non-fiction | 519.5 INT 2017 (Browse shelf) | C-1 | Not For Loan | 29852 | ||
![]() |
EWU Library Circulation Section | Non-fiction | 519.5 INT 2017 (Browse shelf) | C-2 | Available | 29853 | ||
![]() |
EWU Library Circulation Section | Non-fiction | 519.5 INT 2017 (Browse shelf) | C-3 | Available | 29854 |
Includes index.
Table of contents 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.
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.
Computer Science & Engineering Computer Science & Engineering
There are no comments for this item.