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An introduction to statistical learning : with applications in R
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An introduction to statistical learning : with applications in R

by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

This accessible guide introduces key modelling and prediction techniques used to analyse complex datasets. Featuring colour illustrations and practical R tutorials, it is designed for both statisticians and non-statisticians wanting to apply statistical learning.

Accession 14415 ISBN 9781461471370 Publisher Springer
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Communication Complexity Computer Science Culture Education Information Resources Mathematics Prediction Science Social Sciences Statistics
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An introduction to statistical learning : with applications in R
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vernon_slug
an-introduction-to-statistical-learning-with-applications-in-r-gareth-james-daniela-witten-trevor-hastie-robert-tibshirani

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2015
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brief_description
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, 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. --blurb

Subjects & people

authors→ author (initial fill only)
Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten
tags→ tags
Science, Mathematics, Social sciences, Civilisation, Sociology, Communication, Statistics, Computer systems, Electronic data processing, Computers, Data mining, Database searching, Electronic information resource searching, Information retrieval, Information science, Linear models (Statistics), Information resources, Electronic information resources, Big data, Data sets
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