The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
ISBN:9780387848570 read summary

This book itself is a classic for data mining. The one I got is a second hand, and it's in great condition. Shipping is much faster than I expected.

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.

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.

Very entertaining and in-depth review of the topic. But the topic is a lot of different things and there seems to be a bit of a mismatch between the content of the book, the title, and the Amazon categories it is given. Data mining, inference, and predeiction of course, probably have *something* to do with artificial life, but thats not the first thing a reader experts to read about for this kind of topic.

I did enjoy it but expectation management is key. It just ended up being about something a bit different than expected.

I was a good quantitative treatment of several different issues. It could have done a better job of explaining why that particular set of issues was a contiguous group of ideas. I could have imagined them talking about several different concepts as well.

The graphics are great. More stats books should spread their wings with some interest-keeping color.

This should certainly not be the first statistics book you read, or even the second or third book, but when you are ready for it then you should absolutely read it. But be prepared to read it very slowly and digest each page. Its greatest strength is that it shows how much of modern statistics comes down to a few fundamental issues: bias, variance, model complexity, and the curse of dimensionality. There is no free lunch in statistics, methods that claim to avoid these tradeoffs only do so by adding more assumptions about the structure of your data. If your data match the assumptions of such methods, you gain statistical power, but if your data don't match the assumptions then you lose.

By looking closely at the assumptions, the book shows how many contemporary methods that look different are fundamentally similar under the hood.

And in my own work I have adopted their use of open circles for the points in scatterplots. These circles are easier to see than tiny solid dots, but overlapping symbols don't cover each other the way large filled symbols do.

"The Elements of Statistical Learning: Data Mining, Inference and Prediction," 2nd edition by Trevor Hastie, Robert Tibshirani and Jerome Friedman is the classic reference for the recent developments in machine learning statistical methods that have been developed at Stanford and other leading edge universities. Their book covers a broad range of topics and is filled with applications. Much new material has been added since the first edition was published in 2001. Since most of these procedures have been implemented in the open-source program R, this book provides a basic and needed reference for their application. Important estimation procedures discussed include MARS, GAM, Projection Pursuit, Exploratory Projection Pursuit, Random Forest, General Linear Models, Ridge Models and Lasso Models etc. There is an discussion of bagging and boosting and how these techniques can be used. There is an extensive index and the many of the datasets discussed are available from the web page of the book or from other sources on the web. Each chapter has a number of problems that test mastery of the material. I have used material from this book in a number of graduate classes at the University of Illinois in Chicago and have implemented a number of the techniques in my software system B34S. While the 1969 book by Box and Jenkins set the stage for time series analysis using ARIMA and Transfer Function Models, Hastie, Tibshirani and Friedman have produced the classic reference for a wide range of new and important techniques in the area of Machine Learning. For anyone interested in Data Mining this is a must own book.