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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition Hardcover – Apr 12 2011
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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.See all Product Description
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Top Customer Reviews
Perhaps not as good when used as a coursebook, though. Beginners will find that the difficulty of the text varies, even from paragraph to paragraph.
Most Helpful Customer Reviews on Amazon.com (beta)
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. Having that glimpse of where you are going really helps guide you're understanding when you dig in for real.
Third, I wish I had known of R when I first read this; I recommend using it along with some sample data sets to follow along with the text so the concepts become skills not just
abstract relationships to forget. It would probably be worth the extra time, and I wish I had known to do that then.
Fourth, if you are reading this on your own time while making a living, don't expect to finish the book in a month or two.
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 3.5.2 (which can separate Partial Least Squares' design CLAIM of fixing the x-dominance found in principle components analysis from how effective it actually is as fixing such problems).
The biggest issue is who is the book for? This is a mathy book emphasizing deep understanding over mere implementation. Unlike some lesser machine learning books the math is not there for appearances or mere intimidating typesetting: it is there to allow the authors to organize many methods into a smaller number of consistent themes. So I would say the book is for researchers and machine algorithm developers. If you have a specific issue that is making inference difficult you may find the solution in this book. This is good for researchers but probably off-putting for tinkers (as this book likely has methods superior to their current favorite new idea). The interested student will also benefit from this book, the derivations are done well so you learn a lot by working through them.
Finally- don't buy the kindle version, but the print book. This book is satisfying deep reading and you will want the advantages of the printed page (and Amazon's issues in conversion are certainly not the authors' fault).
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. This book will be too mathematical for some, insufficiently rigorous for others, but was just right for me. It will offer too much of a hodge-podge of techniques, miss someone's favorite, or offer just the right balance. In the end, it was the best one for me, so if you're like me (someone with a very solid math base, not a mathematician, who appreciates rigor, but isn't married to it, and who is looking to self-start on this topic.) you'll like 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.
The authors are definitely very well-known researchers in the field, who in particular have written some good papers on a variety of machine learning topics (l1-norm penalized regression, analysis of boosting, to name just two), and thus this book naturally will attract some buzz. It may be very useful to someone like myself who is already familiar with much of what's in the book, or someone who is an expert in the field and just uses it as a quick reference. As a pedagogical tool, however, I think it's pretty much a disaster, and feel compelled to write this as to prevent the typical buyer -- who undoubtedly is buying it to learn and not to use as a reference -- from wasting a lot of money on the wrong text.
Some of the reviews here complain that there is too much math. I don't think that is an issue. If you have decent intuitions in geometry, linear algebra, probability and information theory, then you should be able to cruise through and/or browse in a fairly relaxed way. If you don't have those intuitions, then you are attempting to read the wrong book.
There were a couple of things that I expected (things I happen to know a bit about), but that were missing. On the unsupervised learning side, the discussion of Gaussian mixture clustering was, I thought, a bit short and superficial, and did not bring out the combination of theoretical and practical power that the method offers. On the supervised learning side, I was surprised that a book that dedicates so much time to linear regression finds no room for a discussion of Gaussian process regression as far as I could see (the nearest point of approach is the use of Gaussian radial basis functions [oops: having written that, I immediately came across a brief discussion (S5.8.1) of, essentially, GP regression - though with no reference to standard literature]).
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