عنوان

An introduction to statistical learning :

(Number (ISBN

1461471370

(Number (ISBN

1461471389

(Number (ISBN

9781461471370

(Number (ISBN

9781461471387

Number

dltt

Title Proper

An introduction to statistical learning :

General Material Designation

[Book]

Other Title Information

with applications in R /

First Statement of Responsibility

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.

Edition Statement

[Corrected at sixth printing 2015].

Specific Material Designation and Extent of Item

xiv, 426 pages :

Other Physical Details

illustrations (chiefly color) ;

Dimensions

25 cm.

Series Title

Springer texts in statistics,

Volume Designation

103.

ISSN of Series

1431-875X ;

Text of Note

Includes index.

Text of Note

Introduction -- Statistical learning -- Linear regression -- Classification -- Resampling methods -- Linear model selection and regularization -- Moving beyond linearity -- Tree-based methods -- Support vector machines -- Unsupervised learning.

0

Text of Note

"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.

Parallel Title

Statistical learning

Mathematical models, Problems, exercises, etc.

Mathematical models.

Mathematical statistics, Problems, exercises, etc.

Mathematical statistics.

R (Computer program language)

Statistics.

Models, Statistical.

Statistics as Topic.

Number

Edition

Class number

Book number

Class number

Book Number

Hastie, Trevor

James, Gareth, (Gareth Michael)

Tibshirani, Robert

Witten, Daniela

Date of Transaction

20210107123416.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

270410

Y