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based alternative to ISLR. Hence, this book, An Introduction to Statistical
Learning, With Applications in Python (ISLP), covers the same materials
as ISLR but with labs implemented in Python — a feat accomplished by the
addition of a new co-author, Jonathan Taylor. Several of the labs make use
of the ISLP Python package, which we have written to facilitate carrying out
the statistical learning methods covered in each chapter in Python. These
labs will be useful both for Python novices, as well as experienced users.
The intention behind ISLP (and ISLR) is to concentrate more on the
applications of the methods and less on the mathematical details, so it is
appropriate for advanced undergraduates or master’s students in statistics
or related quantitative elds, 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.
We are grateful to these readers for providing valuable comments on the
rst edition of ISLR: Pallavi Basu, Alexandra Chouldechova, Patrick Dana-
her, Will Fithian, Luella Fu, Sam Gross, Max Grazier G’Sell, Courtney
Paulson, Xinghao Qiao, Elisa Sheng, Noah Simon, Kean Ming Tan, Xin Lu
Tan. We thank these readers for helpful input on the second edition of ISLR:
Alan Agresti, Iain Carmichael, Yiqun Chen, Erin Craig, Daisy Ding, Lucy
Gao, Ismael Lemhadri, Bryan Martin, Anna Neufeld, Geo Tims, Carsten
Voelkmann, Steve Yadlowsky, and James Zou. We are immensely grateful
to Balasubramanian “Naras” Narasimhan for his assistance on both ISLR
and ISLP.
It has been an honor and a privilege for us to see the considerable impact
that ISLR has had on the way in which statistical learning is practiced, both
in and out of the academic setting. We hope that this new Python 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.
-Yogi Berra
Preface
