An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
by Gareth James
Presents key modeling and prediction techniques with R tutorials, covering regression, classification, resampling, trees, SVMs, clustering, and more for practitioners.
About This Book
This book presents some of the most important modeling and prediction techniques, along with relevant applications.
Topics include linear regression, classification, re-sampling 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.
Each chapter contains a tutorial on implementing the analyses and methods presented in R.
The goal is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields.
Reviews
No reviews yet. Be the first to review this book!