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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd ed. 2009. Corr. 7th printing 2013 Edition

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ISBN-13: 978-0387848570
ISBN-10: 0387848576
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Editorial Reviews


From the reviews:

"Like the first edition, the current one is a welcome edition to researchers and academicians equally…. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!" (Book Review Editor, Technometrics, August 2009, VOL. 51, NO. 3)

From the reviews of the second edition:

"This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. … These additions make this book worthwhile to obtain … . In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. The book is so comprehensive that it offers material for several courses." (Klaus Nordhausen, International Statistical Review, Vol. 77 (3), 2009)

“The second edition … features about 200 pages of substantial new additions in the form of four new chapters, as well as various complements to existing chapters. … the book may also be of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d)

“The book would be ideal for statistics graduate students … . This book really is the standard in the field, referenced in most papers and books on the subject, and it is easy to see why. The book is very well written, with informative graphics on almost every other page. It looks great and inviting. You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so.” (Peter Rabinovitch, The Mathematical Association of America, May, 2012)

From the Back Cover

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: 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. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

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Product Details

  • Series: Springer Series in Statistics
  • Hardcover: 745 pages
  • Publisher: Springer; 2nd ed. 2009. Corr. 7th printing 2013 edition (April 12, 2011)
  • Language: English
  • ISBN-10: 0387848576
  • ISBN-13: 978-0387848570
  • Product Dimensions: 9.2 x 6.2 x 1.5 inches
  • Shipping Weight: 3.3 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (52 customer reviews)
  • Amazon Best Sellers Rank: #7,306 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

55 of 59 people found the following review helpful By Enceladus Transit on April 12, 2011
Format: Hardcover Verified Purchase
This review is written from the perspective of a programmer who has sometimes had the chance to choose, hire, and work with algorithms and the mathematician/statisticians that love them in order to get things done for startup companies. I don't know if this review will be as helpful to professional mathematicians, statisticians, or computer scientists.

The good news is, this is pretty much the most important book you are going to read in the space. It will tie everything together for you in a way that I haven't seen any other book attempt. The bad news is you're going to have to work for it. If you just need to use a tool for a single task this book won't be worth it; think of it as a way to train yourself in the fundamentals of the space, but don't expect a recipe book. Get something in the "using R" series for that.

When it came out in 2001 my sense of machine learning was of a jumbled set of recipes that tended to work in some cases. This book showed me how the statistical concepts of bias, variance, smoothing and complexity cut across both fields of traditional statistics and inference and the machine learning algorithms made possible by cheaper cpus. Chapters 2-5 are worth the price of the book by themselves for their overview of learning, linear methods, and how those methods can be adopted for non-linear basis functions.

The hard parts:

First, don't bother reading this book if you aren't willing to learn at least the basics of linear algebra first. Skim the second and third chapters to get a sense for how rusty
your linear algebra is and then come back when you're ready.

Second, you really really want to use the SQRRR technique with this book.
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52 of 58 people found the following review helpful By Craig Garvin on April 4, 2009
Format: Hardcover
This is one of the best books in a difficult field to survey and summarize. Like 'Pattern Recognition', 'Statistical Learning' is an umbrella term for a broad range of techniques of varying complexity, rigor and acceptance by practitioners in the field. The audience for such a text ranges from the user requiring a code library to the mathematician seeking proof of every statement. I sit somewhere in the middle, but more towards the mathematical end. I subscribe to the traditional statistician's view of Machine Learning. It is a term invented in order to avoid having to prove theorems and dodge the rigors of 'real' statistics. However, I strongly support such a course of action. There is an immense need for Machine Learning algorithms, whether they have actual properties or not, and an equal need for books to introduce these topics to people like myself who have a strong mathematical background, but have not been exposed to these techniques.

Hastie & Tibshirani has the most post-it's of any book on my shelf. When my company built an custom multivariate statistical library for our targeted product, we largely followed Hastie & Tibshirani's taxonomy. Their overview of support vector machines is excellent, and I found little of value to me in dedicated volumes like Cristianini & Shawe-Taylor that wasn't covered in Hastie & Tibshirani. Hastie & Tibshirani is another book with excellent visual aides. In addition to some great 2-D representations of complex multidimensional spaces, I thought the 'car going up hill' icon was a very useful cue that the level was going up a notch.

Having praised this book, I can't argue with any of the negative reviews. There is no right answer of where to start or what to cover.
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19 of 19 people found the following review helpful By John Mount on May 17, 2014
Format: Hardcover Verified Purchase
I have been using The Elements of Statistical Learning for years, so it is finally time to try and review it.

The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective. This means you get good derivations of popular methods such as support vector machines, random forests, and graphical models; but each is developed only after the appropriate (and wrongly considered less sexy) statistical framework has already been derived (linear models, kernel smoothing, ensembles, and so on).

In addition to having excellent and correct mathematical derivations of important algorithms The Elements of Statistical Learning is fairly unique in that it actually uses the math to accomplish big things. My favorite examples come from Chapter 3 "Linear Methods for Regression." The standard treatments of these methods depend heavily on respectful memorization of regurgitation of original iterative procedure definitions of the various regression methods. In such a standard formulation two regression methods are different if they have superficially different steps or if different citation/priority histories. The Elements of Statistical Learning instead derives the stopping conditions of each method and considers methods the same if they generate the same solution (regardless of how they claim they do it) and compares consequences and results of different methods. This hard use of isomorphism allows amazing results such as Figure 3.15 (which shows how Least Angle Regression differs from Lasso regression, not just in algorithm description or history: but by picking different models from the same data) and section 3.5.
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