- This book presents some of the most important modeling and prediction techniques, along with relevant applications
- Topics include linear regression, classification, re-sampling 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
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Book Details
Author(s)James, Gareth
PublisherSpringer
ISBN / ASIN1461471370
ISBN-139781461471370
AvailabilityIn Stock
Sales Rank194
CategoryMathematics
MarketplaceUnited States 🇺🇸
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