An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Brand: Springer
Categories: Books, Computers & Technology, Computer Science


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

Publisher: Springer; 1st ed. 2013, Corr. 7th printing 2017 edition (June 25, 2013)
Language: English
Hardcover: 440 pages
ISBN 10: 1461471370
ISBN 13: 978-1461471370
Item Weight: 2.24 pounds
Dimensions: 6.25 x 0.85 x 9.25 inches