An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
by Gareth James
Description
This accessible textbook introduces the core ideas and techniques of statistical learning, showing how data can be used to understand relationships, make predictions, and support decision-making across fields such as biology, finance, marketing, and astrophysics. It covers essential methods including linear regression, classification, resampling, regularization, tree-based models, support vector machines, and clustering, using clear explanations, visualizations, and practical examples. Each chapter includes hands-on guidance for applying the methods in R, making the book useful to students, researchers, and practitioners with a basic background in regression. Designed as a welcoming alternative to more mathematically advanced treatments, it connects statistical theory with real-world data analysis.
Available Editions
paperback
Springer-Verlag New York Inc.
Published Jan 1, 2013
426 pages
ISBN: 9781461471370
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Details
First Published
2013
Language
eng
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