# 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: TextLanguage: English Series: Publisher: New York : Springer, 2017Description: xvi, 426 pages : illustrations (some color) ; 24 cmISBN: 9781461471370 ; 1461471370 (acidfree paper)Other title: Statistical learningSubject(s): Mathematical statistics | Mathematical models | Mathematical statistics -- Problems, exercises, etc | Mathematical models -- Problems, exercises, etc | R (Computer program language) | StatisticsDDC classification: 519.5 LOC classification: QA276 | .I585 2013Online resources: WorldCat Details | Ebook FulltextItem type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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E-Book | EWU Library E-book | Non-fiction | 519.5 INT 2017 (Browse shelf) | Not for loan | ||||

Text | EWU Library Reserve Section | Non-fiction | 519.5 INT 2017 (Browse shelf) | C-1 | Not For Loan | 29852 | ||

Text | EWU Library Circulation Section | Non-fiction | 519.5 INT 2017 (Browse shelf) | C-2 | Available | 29853 | ||

Text | EWU Library Circulation Section | Non-fiction | 519.5 INT 2017 (Browse shelf) | C-3 | Available | 29854 |

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519.5 HOI 2014 Introduction to mathematical statistics / | 519.5 HOT 1978 Introduction to mathematical statistics / | 519.5 HUC 2013 Collected volume of statistical works of Q.M. Husain with commentaries / | 519.5 INT 2017 An introduction to statistical learning : | 519.5 ISI 2001 Introduction to statistics and probability / | 519.5 ISI 2002 Introduction to statistics and probability / | 519.5 ISI 2002 Introduction to statistics and probability / |

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

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