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Author(s): Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Format: Paperback
Publisher: Springer-Verlag New York Inc., United States
Imprint: Springer-Verlag New York Inc.
ISBN-13: 9781071614204, 978-1071614204
Synopsis
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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the meth.
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Details:An Introduction to Statistical Learningwith Applications in R
Author(s): Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Format: Paperback
Publisher: Springer-Verlag New York Inc., United States
Imprint: Springer-Verlag New York Inc.
ISBN-13: 9781071614204, 978-1071614204
Synopsis
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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the meth.
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An Introduction to Statistical Learningwith Applications in R
Author(s): Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Format: Paperback
Publisher: Springer-Verlag New York Inc., United States
Imprint: Springer-Verlag New York Inc.
ISBN-13: 9781071614204, 978-1071614204
Synopsis
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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the meth.
General | |
|---|---|
format | trade paperback |
Language | english |
series | springer texts in statistics ser. |
type | textbook |
Brand | Springer-Verlag New York Inc. |
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