000			01993nam a22002537a 4500
003			OSt
005			20241113111816.0
008			191210b ||||| |||| 00| 0 eng d
020			_a9781461471370
040			_cTata Book House _aICTS-TIFR
050			_aQA 276
100			_aJames Gareth
245			_aAn introduction to statistical learning : with applications in R
260			_aNew York: _bSpringer, _c[c2013]
300			_a426 p
490			_a Springer Texts in Statistics
505			_aCh 1. Introduction Ch 2. Statistical Learning Ch 3. Linear Regression Ch 4. Classification Ch 5. Resampling Methods Ch 6. Linear Model Selection and Regularization Ch 7. Moving Beyond Linearity Ch 8. Tree-Based Methods Ch 9. Support Vector Machines Ch 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, deep learning, survival analysis, multiple testing, 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. --- summary provided by publisher
650			_aStatistics
700			_a Daniela Witten
700			_a Trevor Hastie
700			_a Robert Tibshirani
942			_2lcc _cBK
999			_c2904 _d2904