An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
by Gareth James
An accessible guide to statistical learning methods and their applications. Covering regression, classification, resampling, shrinkage, trees, support vector machines, and clustering, it combines explanations, graphics, and real-world examples with practical R tutorials for readers applying these techniques in science, industry, and beyond.
About This Book
This textbook introduces important statistical learning techniques for modeling and prediction.
Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.
Color graphics and real-world examples help illustrate the methods and their applications.
Each chapter includes a tutorial on implementing the presented analyses in R, a popular open-source statistical software platform.
The material is designed to support practitioners in science, industry, and other fields.
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