Statistical learning refers to a set of tools for making sense of complex datasets. In recent years, we have seen a staggering increase in the scale and scope of data collection across virtually all areas of science and industry. As a result, statistical learning has become a critical toolkit for anyone who wishes to understand data — and as more and more of today’s jobs involve data, this means that statistical learning is fast becoming a critical toolkit for everyone.
One of the first books on statistical learning — The Elements of Statisti- cal Learning (ESL, by Hastie, Tibshirani, and Friedman) — was published in 2001, with a second edition in 2009. ESL has become a popular text not only in statistics but also in related fields. One of the reasons for ESL’s popularity is its relatively accessible style. But ESL is best-suited for indi- viduals with advanced training in the mathematical sciences.
An Introduction to Statistical Learning (ISL) arose from the clear need for a broader and less technical treatment of the key topics in statistical learning. The intention behind ISL is to concentrate more on the applica- tions of the methods and less on the mathematical details. Beginning with Chapter 2, each chapter in ISL contains a lab illustrating how to implement the statistical learning methods seen in that chapter using the popular sta- tistical software package R. These labs provide the reader with valuable hands-on experience.
ISL is appropriate for advanced undergraduates or master’s students in Statistics or related quantitative fields, or for individuals in other disciplines who wish to use statistical learning tools to analyze their data. It can be used as a textbook for a course spanning two semesters.
The first edition of ISL covered a number of important topics, including sparse methods for classification and regression, decision trees, boosting, support vector machines, and clustering. Since it was published in 2013, it has become a mainstay of undergraduate and graduate classrooms across the United States and worldwide, as well as a key reference book for data scientists.
In this second edition of ISL, we have greatly expanded the set of topics covered. In particular, the second edition includes new chapters on deep learning (Chapter 10), survival analysis (Chapter 11), and multiple testing (Chapter 13). We have also substantially expanded some chapters that were part of the first edition: among other updates, we now include treatments of naive Bayes and generalized linear models in Chapter 4, Bayesian addi- tive regression trees in Chapter 8, and matrix completion in Chapter 12. Furthermore, we have updated the R code throughout the labs to ensure that the results that they produce agree with recent R releases.
We are grateful to these readers for providing valuable comments on the first edition of this book: Pallavi Basu, Alexandra Chouldechova, Patrick Danaher, Will Fithian, Luella Fu, Sam Gross, Max Grazier G’Sell, Court- ney Paulson, Xinghao Qiao, Elisa Sheng, Noah Simon, Kean Ming Tan, Xin Lu Tan. We thank these readers for helpful input on the second edi- tion of this book: Alan Agresti, Iain Carmichael, Yiqun Chen, Erin Craig, Daisy Ding, Lucy Gao, Ismael Lemhadri, Bryan Martin, Anna Neufeld, Ge- off Tims, Carsten Voelkmann, Steve Yadlowsky, and James Zou. We also thank Anna Neufeld for her assistance in reformatting the R code through- out this book. We are immensely grateful to Balasubramanian “Naras” Narasimhan for his assistance on both editions of this textbook.
It has been an honor and a privilege for us to see the considerable impact that the first edition of ISL has had on the way in which statistical learning is practiced, both in and out of the academic setting. We hope that this new edition will continue to give today’s and tomorrow’s applied statisticians and data scientists the tools they need for success in a data-driven world.
It’s tough to make predictions, especially about the future.