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.

Color graphics and real-world examples are used to illustrate the methods presented. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. This book presents some of the most important modeling and prediction techniques, along with relevant applications. These labs provide the reader with valuable hands-on experience.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. We have created labs illustrating how to implement each of the statistical learning methods using the popular statistical software package R. In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details.

An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics. But ESL is intended for individuals with advanced training in the mathematical sciences. One of the reasons for ESL’s popularity is its relatively accessible style. ESL has become a popular text not only in statistics but also in related ?elds. One of the ?rst books in this area-The Elements of Statistical Learning (ESL) (Hastie, Tibshirani, and Friedman)-was published in 2001, with a second edition in 2009. People with statistical learning skills are in high demand. With the explosion of “Big Data” problems, statistical learning has become a very hot ?eld in many scienti?c areas as well as marketing, ?nance, and other business disciplines. The ?eld encompasses many methods such as the lasso and sparse regression, classi?cation and regression trees, and boosting and support vector machines. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. Statistical learning refers to a set of tools for modeling and understanding complex datasets.

An introduction to statistical learning