Format:
xv, 607 Seiten :
,
Illustrationen, Diagramme.
Edition:
Second edition
ISBN:
978-1-0716-1417-4
,
978-1-0716-1420-4
,
1-0716-1417-7
Series Statement:
Springer texts in statistics
Content:
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 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.
Additional Edition:
Erscheint auch als Online-Ausgabe ISBN 978-1-4614-7138-7
Additional Edition:
Erscheint auch als Online-Ausgabe ISBN 978-1-0716-1418-1
Language:
English
Subjects:
Computer Science
,
Economics
,
Mathematics
,
Psychology
,
Sociology
Keywords:
Statistik
;
R
;
Statistik
;
Maschinelles Lernen
Author information:
Hastie, Trevor 1953-
Author information:
Tibshirani, Robert 1956-
