000			02497nam a2200241Ia 4500
003			NULRC
005			20250520102814.0
008			250520s9999 xx 000 0 und d
020			_a9781461471370
040			_cNULRC
050			_aQA 276 .I58 2013
100			_aJames, Gareth _eauthor
245		3	_aAn introduction to statistical learning with applications in R / _cGareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
260			_aNew York : _bSpringer, _cc2013
300			_axiv, 426 pages : _billustrations ; _c24 cm.
365			_bUSD44.49
504			_aIncludes index.
505			_a1. Introduction -- 2. Statistical learning -- 3. Linear regression -- 4. Classification -- 5. Resampling methods -- 6. Linear model selection and regularization -- 7. Moving beyond linearity -- 8. Tree-based methods -- 9. Support vector machines -- 10. Unsupervised learning.
520			_aAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data.
650			_aMATHEMATICAL STATISTICS
700			_aWitten, Daniela ;Hastie, Trevor;Tibshirani, Robert _eco-author;co-author;co-author
942			_2lcc _cBK
999			_c15756 _d15756