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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 2nd ed. 2021 Edition

by Gareth James (Author), Daniela Witten (Author), Trevor Hastie (Author),
4.6 4.6 out of 5 stars 307 ratings
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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, deep learning, survival analysis, multiple testing, 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.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Previous page
  1. ISBN-10
    1071614177
  2. ISBN-13
    978-1071614174
  3. Edition
    2nd ed. 2021
  4. Publisher
    Springer
  5. Publication date
    July 30, 2021
  6. Part of series
    Springer Texts in Statistics
  7. Language
    English
  8. Dimensions
    6.5 x 1.25 x 9.5 inches
  9. Print length
    622 pages
  10. See all details
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Editorial Reviews

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, deep learning, survival analysis, multiple testing, 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.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Product details

  • Publisher ‏ : ‎ Springer; 2nd ed. 2021 edition (July 30, 2021)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 622 pages
  • ISBN-10 ‏ : ‎ 1071614177
  • ISBN-13 ‏ : ‎ 978-1071614174
  • Item Weight ‏ : ‎ 2.63 pounds
  • Dimensions ‏ : ‎ 6.5 x 1.25 x 9.5 inches
  • Customer Reviews:
    4.6 4.6 out of 5 stars 307 ratings

About the authors

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  1. Robert Tibshirani

    Robert Tibshirani

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    Full content visible, double tap to read brief content.

    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.

    See more on the author's page
  2. Gareth James

    Gareth James

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    Full content visible, double tap to read brief content.

    Discover more of the author’s books, see similar authors, read author blogs and more

    See more on the author's page
  3. Daniela Witten

    Daniela Witten

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    Full content visible, double tap to read brief content.

    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

    See more on the author's page
  4. Trevor Hastie

    Trevor Hastie

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    Full content visible, double tap to read brief content.

    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.

    See more on the author's page
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Customer reviews

4.6 out of 5 stars
4.6 out of 5
307 global ratings
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5 Stars
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I used this book in my statistical learning & data mining course last summer. At the time, the pdf version of this book was available from my university library so I didn't get the hard copy until now. The reason I decided to get the hard copy is that the theory/conceptual part is well-balanced between proper depth and easy-to-understand. Even though I'm now doing a Machine Learning training program in Python, I still recall the rationale of different models that were well explained in this book. So I've decided to get a permanent copy.
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Mark B. Fernandez
5.0 out of 5 stars The Most Accessible Statistics Textbook
Reviewed in the United States on January 30, 2024
Verified Purchase
The authors Hastie and Tibshirani are legends in the stats world, creating GAM and LASSO respectively. Their other textbook "The Elements of Statistical Learning" is geared for PhD students. This textbook is very accessible, with figures and lots of sample code. The target audience is any aspiring data scientist who can learn to code and wants to actually understand what the code/models are doing (but doesn't need to be able to derive all the original math by hand). In addition to teaching different analyses, this book does a great job on explaining key statistical analysis concepts, like bias vs variance tradeoff, k-fold cross-validation, bootstrapping, finding the right balance in model complexity for your dataset, etc. There is both an R and a Python edition. The 2nd edition includes 3 new chapters on survival analysis, multiple testing, and neural nets. There is a free Stanford MOOC that uses this text.
2 people found this helpful
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juan-jose parra-pagan
5.0 out of 5 stars Bayes rules !
Reviewed in the United States on March 11, 2024
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Es un buen libro, practico y actualizado, con una version en online de junio 2023, que no cambia la edicion impresa
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Stephen Maharaj
5.0 out of 5 stars Easy to understand
Reviewed in the United States on November 18, 2023
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Practical approach to Statistical Learning. Well written by pioneers in the field.

There is a free pdf and accompanying online course.
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Cielo
5.0 out of 5 stars Buy the hardcover
Reviewed in the United States on February 28, 2023
Verified Purchase
I used this book in my statistical learning & data mining course last summer. At the time, the pdf version of this book was available from my university library so I didn't get the hard copy until now. The reason I decided to get the hard copy is that the theory/conceptual part is well-balanced between proper depth and easy-to-understand. Even though I'm now doing a Machine Learning training program in Python, I still recall the rationale of different models that were well explained in this book. So I've decided to get a permanent copy.
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Cielo
5.0 out of 5 stars Buy the hardcover
Reviewed in the United States on February 28, 2023
I used this book in my statistical learning & data mining course last summer. At the time, the pdf version of this book was available from my university library so I didn't get the hard copy until now. The reason I decided to get the hard copy is that the theory/conceptual part is well-balanced between proper depth and easy-to-understand. Even though I'm now doing a Machine Learning training program in Python, I still recall the rationale of different models that were well explained in this book. So I've decided to get a permanent copy.
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6 people found this helpful
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Jessica Macaluso
4.0 out of 5 stars Good
Reviewed in the United States on October 26, 2023
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Needed it for class and yep learned things.
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Monika Cunningham
5.0 out of 5 stars The BEST stats book!!
Reviewed in the United States on June 12, 2023
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I took a stats class during my masters that used the 1st edition, the additional topics in the 2nd edition make it even better. Fantastic writing, probably the only stats book I’ve read cover-to-cover!
5 people found this helpful
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Akimon85
5.0 out of 5 stars Excellent book
Reviewed in the United States on March 8, 2022
Verified Purchase
I recently used this book along with a couple others in a graduate level ML course. IMO it was the best in terms of striking a good balance; containing enough detail to help grasp theory but not so much that it becomes a slog to get through. I used the Ebook in class and liked it enough to buy a hardcopy. Unfortunately, the print font size is quite small. Overall dimension is smaller than listed on Amazon. Maybe that was how big the 1st edition was?
3 people found this helpful
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Carl McQueen
5.0 out of 5 stars Great book to broaden understanding
Reviewed in the United States on November 14, 2021
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This book is an amazing resource to get your understanding across many different methods in line. One of the greatest tools of a data scientist and statistician in general is knowledge of best method, or best tool, for a task. Many solutions in data science right now go far too heavily toward one size fits all and this books helps one understand why knowing how to read your results and why to use the method to solve it really, really matter.
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Leonardo Bastos
5.0 out of 5 stars Ótimo livro introdutório
Reviewed in Brazil on June 12, 2023
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Excelente livro introdutório para o aprendizado estatístico. Redefine muito bem conceitos estatísticos tradicionais sob um olhar mais atual no contexto do aprendizado de máquina. Exemplos na linguagem R, o que é ótimo para os alunos "meterem a mão na massa". Uma referência essencial para estatísticos e cientistas de dados.
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Danielle B. Fonseca
5.0 out of 5 stars Good Quality
Reviewed in Canada on February 25, 2023
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I like a lot the decision tree with codes
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Arturo Sbr
5.0 out of 5 stars Greatest Data Science book ever (coming from someone who hates R)
Reviewed in Mexico on October 12, 2022
Verified Purchase
I reviewed this book for a class in my master's program and I loved it from start to end.
I already knew most of the concepts but became hooked because of how clear the explanations are. The authors convey complex ideas with remarkable simplicity, and for that, I think this is the most important book for data scientists.
I am an avid opposer of the R programming language (ew) and even I enjoyed the applied programming parts of the book.
In all honesty, the applications in R are very good, but it's not the main focus of the book. I think people should read this to understand the inner workings of the most popular AI algorithms instead of learning how to train predictive models (especially in R, haha).
Overall, I think this is a great book for beginners and veterans alike. I would not hesitate to recommend this book to anyone interested in statistics, data and AI.
2 people found this helpful
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Ricardo Salas
5.0 out of 5 stars a MUST reading
Reviewed in Spain on April 25, 2024
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wonderfull book, I am currently studying a master in Bionformatics and needed to brush my forgotten lessons of Statistics. Amazed how the authors are able to explain the most advanced and difficult concepts skiping the mathematics below, for example the subject of hyperplanes is so amazingly exposed that it should be given as an role model of teaching and turning a difficult subject into an accesible one.I recommed this book with all my heart¡¡
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A reader
5.0 out of 5 stars Baby version of a classic in Machine Learning
Reviewed in Italy on April 15, 2024
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Very thorough introduction to machine learning, to be followed by The elements of Statistical Learning, by Hastie Tibshirani James
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