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An introduction to statistical learning : with applications in R / Gareth James... [et al.].

Contributor(s): James, Gareth [author.] | Witten, Daniela [author.] | Hastie, Trevor [author.] | Tibshirani, Robert [author.]Material type: TextTextLanguage: English Series: Publication details: New York : Springer, 2017. Description: 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 Fulltext
Contents:
TOC 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.
Summary: 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.
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Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book Dr. S. R. Lasker Library, EWU
E-book
Non-fiction 519.5 INT 2017 (Browse shelf(Opens below)) Not for loan
Text Text Dr. S. R. Lasker Library, EWU
Reserve Section
Non-fiction 519.5 INT 2017 (Browse shelf(Opens below)) C-1 Not For Loan 29852
Text Text Dr. S. R. Lasker Library, EWU
Circulation Section
Non-fiction 519.5 INT 2017 (Browse shelf(Opens below)) C-2 Available 29853
Text Text Dr. S. R. Lasker Library, EWU
Circulation Section
Non-fiction 519.5 INT 2017 (Browse shelf(Opens below)) C-3 Checked out 25/03/2024 29854
Total holds: 0

Includes index.

TOC 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.

CSE CSE

Sagar Shahanawaz

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