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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: TextTextSeries: Publisher: New York : Springer, 2017Description: xvi, 426 pages : illustrations (some color) ; 24 cm.ISBN: 9781461471370 ; 1461471370 (acidfree paper).Other title: Statistical learning.Subject(s): Mathematical statistics | Mathematical models | Mathematical statistics -- Problems, exercises, etc | Mathematical models -- Problems, exercises, etc | R (Computer program language) | StatisticsDDC classification: 519.5 Online resources: WorldCat Details | Ebook Fulltext
Contents:
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.
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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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.

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