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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani is a science book available to read on EtoBox.

What is An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103) about?

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 modelling 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. Colour 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

Who reads An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103)?

Author

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Publisher

Springer Science + Business Media

Published

2013

Language

EN

ISBN

9781461471387

Category

science

Subjects

Mathematics, Fiction, Reference