Umfang:
xv, 607 Seiten :
,
Illustrationen, Diagramme.
Ausgabe:
Second edition
ISBN:
978-1-0716-1417-4
,
978-1-0716-1420-4
,
1-0716-1417-7
Serie:
Springer texts in statistics
Inhalt:
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.
Weitere Ausg.:
Erscheint auch als Online-Ausgabe ISBN 978-1-4614-7138-7
Weitere Ausg.:
Erscheint auch als Online-Ausgabe ISBN 978-1-0716-1418-1
Sprache:
Englisch
Fachgebiete:
Informatik
,
Wirtschaftswissenschaften
,
Mathematik
,
Psychologie
,
Soziologie
Schlagwort(e):
Statistik
;
R
;
Statistik
;
Maschinelles Lernen
;
Lehrbuch
;
Einführung
Mehr zum Autor:
Hastie, Trevor 1953-
Mehr zum Autor:
Tibshirani, Robert 1956-
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