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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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ISBN
Brand
Springer
Author
Gareth James, Trevor Hastie, Daniela Witten, Robert Tibshirani
Book Title
An Introduction to Statistical Learning: with Applications in R (
Format
Hardcover
Genre
Probability & statistics
Item Height
235mm
Item Weight
1014g
Item Width
155mm
Language
English
Number of Pages
426 Pages
Publication Name
An Introduction to Statistical Learning: with Applications in R
Publication Year
2017
Publisher
Springer-Verlag NY Inc.
Subject
Computer Science, Mathematics
Type
Textbook
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
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
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