An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
- Other Title:
- Statistical learning
- [Corrected at 6th printing 2015].
- New York : Springer : Springer Science+Business Media, 2015.
- Springer texts in statistics.
Springer texts in statistics, 1431-875X ; 103
xiv, 426 pages : illustrations (chiefly color) ; 25 cm
- Mathematical statistics.
Mathematical statistics -- Problems, exercises, etc.
Mathematical models -- Problems, exercises, etc.
R (Computer program language)
- Medical subjects:
- Models, Statistical.
Statistics as Topic.
- Problems and exercises.
- "An 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. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields. Analyses and methods are presented in R. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"--Publisher description.
Linear model selection and regularization
Moving beyond linearity
Support vector machines
- Includes index.
- Local notes:
- Acquired for the Penn Libraries with assistance from the James Hosmer Penniman Book Fund.
- James Hosmer Penniman Book Fund.
James, Gareth (Gareth Michael), author.
Witten, Daniela, author.
Hastie, Trevor, author.
Tibshirani, Robert, author.
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