An introduction to statistical learning: with applications in Python

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, marketing, and astrophysics in the past twenty years. Thi...

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Bibliographische Detailangaben
Hauptverfasser: James, Gareth (VerfasserIn), Witten, Daniela (VerfasserIn), Hastie, Trevor 1953- (VerfasserIn), Tibshirani, Robert 1956- (VerfasserIn), Taylor, Jonathan E. (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Cham Springer 2023
Ausgabe:1st ed. 2023
Schriftenreihe:Springer texts in statistics
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Zusammenfassung: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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.
Beschreibung:1 Online-Ressource (XV, 607 Seiten)
ISBN:9783031387470
ISSN:2197-4136
DOI:10.1007/978-3-031-38747-0