An Introduction to Statistical Learning

with Applications in R

Nonfiction, Science & Nature, Mathematics, Statistics, Computers, Application Software
Cover of the book An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer New York
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ISBN: 9781461471387
Publisher: Springer New York Publication: June 24, 2013
Imprint: Springer Language: English
Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
ISBN: 9781461471387
Publisher: Springer New York
Publication: June 24, 2013
Imprint: Springer
Language: English

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 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.

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 covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 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.

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 covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

More books from Springer New York

Cover of the book National Intellectual Capital by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Computational Challenges in the Geosciences by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Fractional Dynamics and Control by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Mode 3 Knowledge Production in Quadruple Helix Innovation Systems by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book The Welfare State in Post-Industrial Society by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Nanotechnology in Dermatology by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Recommender Systems for Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book CMOS Integrated Capacitive DC-DC Converters by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Dynamic Process Methodology in the Social and Developmental Sciences by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Community-Based Operations Research by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Carbon Capture by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Sleep Medicine by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Tuberculosis by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Cover of the book Resilience Interventions for Youth in Diverse Populations by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy