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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) Hardcover – April 12, 2011

ISBN-13: 978-0387848570 ISBN-10: 0387848576 Edition: 2nd ed. 2009. Corr. 7th printing 2013

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Editorial Reviews

Review

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: 4.0 out of 5 stars  See all reviews (39 customer reviews)
  • Amazon Best Sellers Rank: #8,210 in Books (See Top 100 in Books)

Customer Reviews

It is well written and very comprehensive.
Kevin S. Gray
The book is excellent if you want to use it as a reference and study machine learning by yourself.
GM
If you don't have those intuitions, then you are attempting to read the wrong book.
S. Matthews

Most Helpful Customer Reviews

29 of 32 people found the following review helpful By Matthew Grosso 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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40 of 46 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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90 of 112 people found the following review helpful By SP, ML, Stats on February 16, 2010
Format: Hardcover Verified Purchase
I have three texts in machine learning (Duda et. al, Bishop, and this one), and I can unequivocally say that, in my judgement, if you're looking to learn the key concepts of machine learning, this one is by far the worst of the three. Quite simply, it reads almost as a research monologue, only with less explanation and far less coherence. There's little/no attempt to demystify concepts to the newcomer, and the exposition is all over the map. There simply isn't a clear, coherent path that the authors set out to go on in writing a given chapter of this text; it's as if they tried to squeeze every bit of information of the most recent results into the chapter, with little regard to what such a decision might do to the overall readability of the text and the newcomer's understanding. To people who might disagree with me on this point, I'd recommend reading a chapter in Bishop's text and comparing it to similar content in this one, and I think you'll at least better appreciate my viewpoint, if not agree with it.

So you might be wondering, why do I even own the text given my opinion? Well, two reasons: (1) it cost 25 dollars through Springer and a contract they have with my university (definitely look into this before buying on Amazon!), and (2) if you actually already know the concepts, it is quite useful as a summary of what's out there. So to those who understand the basics of machine learning, and also have exposure to greedy algorithms, convex optimization, wavelets, and some other often-utilized methods in the text, this makes for a pretty good reference.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) + An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) + Applied Predictive Modeling
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