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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 1st ed. 2013, Corr. 7th printing 2017 Edition

by Gareth James (Author), Daniela Witten (Author), Trevor Hastie (Author),

4.7 1,918 ratings

Part of: Springer Texts in Statistics (108 books)

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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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling 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. 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.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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Editorial Reviews

Review

“Data and statistics are an increasingly important part of modern life, and nearly everyone would be better off with a deeper understanding of the tools that help explain our world. Even if you don’t want to become a data analyst―which happens to be one of the fastest-growing jobs out there, just so you know―these books are invaluable guides to help explain what’s going on.” (Pocket, February 23, 2018)

Review

"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University)

From the Back Cover

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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling 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. 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.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

About the Author

Gareth James is a professor of data sciences and operations at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.

Daniela Witten is an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning.

Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.

Product details

Publisher ‏ : ‎ Springer; 1st ed. 2013, Corr. 7th printing 2017 edition (June 25, 2013)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 440 pages
ISBN-10 ‏ : ‎ 1461471370
ISBN-13 ‏ : ‎ 978-1461471370
Item Weight ‏ : ‎ 2.31 pounds
Dimensions ‏ : ‎ 7.7 x 1 x 9.4 inches

Best Sellers Rank: #89,525 in Books (See Top 100 in Books)
- #12 in Mathematical & Statistical Software
- #84 in Probability & Statistics (Books)
- #177 in Artificial Intelligence & Semantics

Customer Reviews:
4.7 1,918 ratings

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Trevor Hastie
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Trevor Hastie is the John A Overdeck Professor of Statistics at
Stanford University. Hastie is known for his research in applied
statistics, particularly in the fields of statistical modeling, bioinformatics
and machine learning. He has published six books and over 200
research articles in these areas. Prior to joining Stanford
University in 1994, Hastie worked at AT&T Bell Laboratories for nine
years, where he contributed to the development of the statistical modeling environment
popular in the R computing system. He received a B.Sc. (hons) in statistics
from Rhodes University in 1976, a M.Sc. from the University of Cape
Town in 1979, and a Ph.D from Stanford in 1984. In 2018 he was elected
to the U.S. National Academy of Sciences. He is a dual citizen of the
United States and South Africa.
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Daniela Witten
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Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair. She is the recipient of a NIH Director's Early Independence Award, a NSF CAREER Award, a Sloan Research Fellowship, and a Simons Investigator Award. For more, see www.danielawitten.com
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Robert Tibshirani
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Robert Tibshirani (born July 10, 1956) is a Professor in the Departments of Statistics and Health Research and Policy at Stanford University. He was a Professor at the University of Toronto from 1985 to 1998. In his work, he develops statistical tools for the analysis of complex datasets, most recently in genomics and proteomics.
His most well-known contributions are the LASSO method, which proposed the use of L1 penalization in regression and related problems, and Significance Analysis of Microarrays. He has also co-authored three well-known books: "Generalized Additive Models", "An Introduction to the Bootstrap", and "The Elements of Statistical Learning", the last of which is available for free from the author's website.
Bio from Wikipedia, the free encyclopedia. Photo by Tibshirani (i took this photo) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons.
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Gareth James
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JamesG

This book is more like a ticket of a beautiful park

Reviewed in the United States on January 24, 2018

Verified Purchase

This is a wonderful book for an intro to the world of statistical learning. As an engineering students, it is very approachable and readable. It took me 2 days to finish all chapters, without exercise. To read through the chapters, it's much more enjoyable than reading other math/stat books, since the ideas behind each model or algorithms are very clear even intuitive, a lot of well-made plots make the understanding even easier. I would like to recommend to anyone who want to enter the world of statistical learning.
However, from a graduate level student, I would say this book is more suitable for a undergrad stat or related field student, practitioners, or an entry level graduate student who is not majoring in stat or math. The ideas are much more intuitive than rigorous. If only use such book to do any real world problem, even though they talk about cross validation or something a little bit involved, practitioners may either came across so much problems in statistical analysis, or come to a wrong conclusion. Not saying the methods within this book is wrong, but without deep understanding of some theories or rigorous assumpions of the methods, pure blind trying different algorithms to find lowest MSE may not be suitable for some cases.
Still, this is a wonderful book for two cases:
1. If you have some background in theoretical or mathematical statistics and want to gain some knowledge of applied methods, this book will be wonderful for you to find applications with your theoretical knowledge;
2. If you have few knowledge about rigorous statistics, but want to enter the world of statistical/machine learning, this one is very suitable to trigger your interest for reading deeper and more rigorous books, such as ESL.
For myself, this books is more like a ticket. I have the ticket of a beautiful state park. I use it to cross the gate of the park, but stand near the gate to give an overlook of the beautiful scenes of the park. The map described on the ticket is only contained the main road of the park. If you want to check more beautiful scenes, you need more work, more tickets, more tools to take an adventure within this park for quite a while.

20 people found this helpful

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Sandro Saitta

Written by statisticians for non-statisticians

Reviewed in the United States on May 12, 2016

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Without any suspense, "An Introduction to Statistical Learning" (ISL) by James, Witten, Hastie and Tibshirani is a key book in the Data Science literature. I would summarize it as a book written by statisticians for non-statisticians. Indeed, while the book "The Elements of Statistical Learning" was heavy on theory and equations, ISL is the practical counterpart. The book is very clear and contains only theory you need to understand the data mining algorithms covered. It's thus a invaluable resource for Data Scientists who don't need all theorems and proofs related to a given algorithm, but still need to understand how it works.

Several examples are provided to illustrate each algorithm. Each chapter contains a section with R labs, showing the code needed to move from reading the book to doing data science. The book has a strong emphasis on linear regression and related non-linear approaches (more than half of the book). This lets very few place to other approaches such as decision trees and SVM, which are still covered. The final chapter rapidly covers PCA and clustering.

Although the book is targeted towards a larger audience than statisticians, you shouldn't be afraid of equations (by the way, if you look for an excellent book covering data science algorithms with nearly no equation, have a look at "Data Science for Business" from Provost and Fawcett). With such an excellent book, we are obviously more exigent and I was looking for more coverage of validity indices for clustering, Support Vector Regression, and a final chapter about trends and challenges. In conclusion, ISL is the definitive resource for Data Scientists who want to get the correct level of statistical background in their work.

4 people found this helpful

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Joseph Johnson

cover all of your bases

Reviewed in the United States on January 26, 2014

Verified Purchase

If you want to build a comprehensive machine learning library, this would be the first book to purchase. While it does cover all of the basics, it is not watered down by any means. (I had the same fear as BK Reader) I found the following to be especially helpful;

1. Straight talk - These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones.
2. Emphasis on subjects that are not heavily addressed in most ML books - They thoroughly cover the challenges of high-dimensionality, data cleaning, and standardization. They do not limit their attention to these subjects to one chapter. They bring them up continually throughout the book.
3. Expertise - Dr. Hastie and Dr. Tibshirani are two of the thought leaders in statistical learning. You can be assured that you are learning from the best.
4. Many levels of depth - While the book does cover the basics, it is not watered down by any means. (I had the same worry as BK Reader) There is a great deal for any student of statistics; beginner or advanced.
5. R code - You are given enough code and examples to gain confidence in your ability to independently perform excellent analysis and modeling.
6. The concepts are just plain exciting! - You will feel an excitement as you discover and re-discover the algorithms they present.

The book is a standard work along with Elements of Statistical Learning and Pattern Recognition and Machine Learning (the Bayesian approach). If you enjoy the book, you may also want to consider Applied Predictive Modeling. It has the same style and approach.

45 people found this helpful

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Guillermo Martinez Dibene

Great introductory survey to many statistical tools and R code

Reviewed in Canada on February 1, 2022

Verified Purchase

This book is quite fantastic for an introductory level. I have a specialised training in mathematics and this books puts me up to date on several topics and algorithms without having to deal much time in the mathematical technicalities (for that, there are specialised treatises on each subject). The R code is quite good and although I think some of their advice is not appropriate (e.g. the attach function is in R only for legacy purposes but shouldn't be used since it overrides other functions, including base, aka default, functions), it is workable and so it's a great investment. The book is introductory focusing mostly on intuition and how to do, little time is spent in mathematical formalities, so that's a plus. I think there is a NEWER edition than the one I reviewed but otherwise I suspect the newer edition will be just better. Recommended.

sandy

The Holy Grail of Machine Learning

Reviewed in India on August 7, 2023

Verified Purchase

This book is unparalleled in its coverage of Statistical/Machine Learning. It is comprehensive, easy to understand, and provides numerous examples to aid in comprehension. It also helps develop intuition and serves as a foundation for more mathematically advanced ML topics. Tip: Even if you prefer Python over R, consider using this book for theory and finding other resources for Python implementations.

2 people found this helpful

Cliente de Amazon

El mejor libro para iniciar con rigor a hacer ciencia de datos

Reviewed in Mexico on July 3, 2020

Verified Purchase

Es un libro introductorio, pero que toca las bases matemáticas de los métodos estadísticos mas utilizados en análisis de datos.

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Eqf

Imprescindible

Reviewed in Spain on February 10, 2021