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