An introduction to statistical learning : with applications in R / Gareth James... [et al.].Material type: TextLanguage: 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
|Item type||Current library||Collection||Call number||Copy number||Status||Date due||Barcode||Item holds|
|E-Book||EWU Library E-book||Non-fiction||519.5 INT 2017 (Browse shelf(Opens below))||Not for loan|
|Text||EWU Library Reserve Section||Non-fiction||519.5 INT 2017 (Browse shelf(Opens below))||C-1||Not For Loan||29852|
|Text||EWU Library Circulation Section||Non-fiction||519.5 INT 2017 (Browse shelf(Opens below))||C-2||Available||29853|
|Text||EWU Library Circulation Section||Non-fiction||519.5 INT 2017 (Browse shelf(Opens below))||C-3||Available||29854|
Browsing EWU Library shelves, Shelving location: E-book Close shelf browser (Hides shelf browser)
|519.5 DOI 2008 An introduction to generalized linear models /||519.5 HOF 2009 A first course in Bayesian statistical methods /||519.5 HOI 2005 Introduction to mathematical statistics /||519.5 INT 2017 An introduction to statistical learning :||519.5 JIL 2007 Linear and generalized linear mixed models and their applications /||519.5 KAS 2002 The statistical analysis of failure time data /||519.5 KOS 2009 Statistical analysis of network data :|
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.
There are no comments on this title.