An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.

Contributor(s):

Material type:

TextSeries: Springer texts in statistics ; 417Publication details: New York : Springer, 2013.Description: xvi, 426 p. : ill. b&w and col. 24 cmContent type:

text

ISBN:

9781461471370

Subject(s):

DDC classification:

519.5

Contents:

Ch. 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.

Summary: 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.

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Item type	Current library	Call number	Status	Barcode
Books	Marbella International University Centre Library	519.5 INT int (Browse shelf(Opens below))	Available	11788

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Includes index.

Ch. 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.

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.

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