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An Introduction to Statistical Learning - with Applications in R (Hardcover, 2nd ed. 2021) Loot Price: R1,850
Discovery Miles 18 500
You Save: R242 (12%)
An Introduction to Statistical Learning - with Applications in R (Hardcover, 2nd ed. 2021): Gareth James, Daniela Witten,...

An Introduction to Statistical Learning - with Applications in R (Hardcover, 2nd ed. 2021)

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Series: Springer Texts in Statistics

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List price R2,092 Loot Price R1,850 Discovery Miles 18 500 | Repayment Terms: R173 pm x 12* You Save R242 (12%)

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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 naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

General

Imprint: Springer-Verlag New York
Country of origin: United States
Series: Springer Texts in Statistics
Release date: July 2021
First published: 2021
Authors: Gareth James • Daniela Witten • Trevor Hastie • Robert Tibshirani
Dimensions: 235 x 155 x 30mm (L x W x T)
Format: Hardcover
Pages: 607
Edition: 2nd ed. 2021
ISBN-13: 978-1-07-161417-4
Categories: Books > Science & Mathematics > Mathematics > Probability & statistics
Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN: 1-07-161417-7
Barcode: 9781071614174

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