Exceptional book; has some flaws (Re: 1st edition) | The Elements of Statistical Learning: Data Mining,...

Interesting, a bit random, and perhaps misclassified | The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series ... | Trevor Hastie, Robert Tibshirani, ...

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	• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series ... Trevor Hastie, Robert Tibshirani, ... Springer, 2009 - 746 pages average customer review:based on 38 reviews view larger image
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highly recommended

Excellent but assumes considerable background

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.

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Exceptional book; has some flaws (Re: 1st edition)

The book is excellent if you want to use it as a reference and study machine learning by yourself. It's quite comprehensive and deep in areas in which authors are most familiar & famous (frequentist approach, ensemble techniques, maximum likelihood and its variations, lasso). I would recommend you Bishop's machine learning book as an alternative if you want to gain a deeper understanding of Bayesian techniques--that one is more readable as well. Hastie et al's book is just ok from a didactic point of view. The real world examples are complicated to follow (would prefer simpler synthetic data sets). Some descriptions & explanations are too terse--a price to pay for comprehensiveness in a small volume. Overall, a great effort and useful contribution. You'll most likely need to check out other sources to gain a deeper understanding of some of the topics discussed. See MIT machine learning lecture notes (available online) & Bishop's book if you're a newcomer to machine learning.

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Interesting, a bit random, and perhaps misclassified

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.

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Review of Elements of Statistical Learning

"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.

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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 & 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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