An Introduction to Statistical Learning with Applications in R

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  • Engels
  • Hardcover
  • 9781071614174
  • 30 juli 2021
  • 607 pagina's
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Gareth James

"Gareth David James (born 1 December 1984) is an English cricketer. James is a right-handed batsman who bowls leg breaks. He was born at Walthamstow, London.

(Bron: Wikipedia. Beschikbaar onder de licentie Creative Commons Naamsvermelding/Gelijk delen.)"

Samenvatting

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.

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.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Productspecificaties

Inhoud

Taal
en
Bindwijze
Hardcover
Oorspronkelijke releasedatum
30 juli 2021
Aantal pagina's
607
Illustraties
Nee

Betrokkenen

Hoofdauteur
Gareth James
Tweede Auteur
Daniela Witten
Co Auteur
Daniela Witten

Overige kenmerken

Editie
2
Product breedte
155 mm
Product lengte
235 mm
Studieboek
Ja
Verpakking breedte
160 mm
Verpakking hoogte
31 mm
Verpakking lengte
239 mm
Verpakkingsgewicht
1191 g

EAN

EAN
9781071614174

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