Preface
Statistical learning refers to a set of tools for modeling and understanding
complex datasets. It is a recently developed are a in statistics, and blends
with parallel developments in computer science, and in pa rticular machine
learning. The field encompasses many methods such as the lasso and sparse
regression, classification and regression trees, and boosting and support
vector machines.
With the explosio n of “Big Data” proble ms statistical learning has be-
come a very hot field in many scientific areas as well as marketing, finance
and other business disciplines. People with s tatistical learning skills are in
high demand.
One of the first b ooks in this area — The Elements of Statistical Learn-
ing (ESL) (Hastie, Tibshirani, and Friedman) — was published in 2001 ,
with a second edition in 2 009. ESL has become a popular text not only
in statistics but also in related fields. One of the reasons for ESL’s popu-
larity is its relatively accessible style. But ESL is intended for individuals
with advanced training in the mathematical sciences. An Introduction to
Statistical Learning (ISL) arose from the perc eived nee d for a broader and
less technical treatment of these topics. 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. We have created labs
illustrating how to implement each of the statistical learning methods using
the popular statistical software package R. Thes e labs provide the reader
with valuable hands-on experience.
This book is appropriate for advanced undergradua tes or master’s stu-
dents in Statistics or related quantitative fields, or for individuals in other
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