An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 2nd ed. 2021 Edition

$30.00

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, deep learning, survival analysis, multiple testing, 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.

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 2nd ed. 2021 Edition

$30.00