The book is really helpful and was being delivered to me in a timely fashion.

It gives a complete overview and middle-depth discussions on a wide thematic statistics. Additionally provides methodological elements for making decisions on the implementation of specific techniques. Very good book. I'm an economist and statistical and I was very useful.

Data mining is a field developed by computer scientists but many of its crucial elements are imbedded in important and subtle statistical concepts. Statisticians can play an important role in the development of this field but as was the case with artificial intelligence, expert systems and neural networks the statistical research community has been slow to respond. Hastie, Tibshirani and Friedman are changing this.
Friedman has been a major player in pattern recognition of high dimensional data, in tree classification, regularized discriminant analysis and multivariate adaptive regression splines. He has also done some exciting new research on boosting methods.

Hastie and Tibshirani invented additive models which are very general types of regression models. Tibshirani invented the lasso method and is a leader among the researchers on bootstrap. Hastie invented principal curves and surfaces.

These tools and the expertise of these authors make them naturals to contribute to advances in data mining. They come with great expertise and see data mining from the statistical perspective. They see it as part of a more general process of statistical learning from data.

The book is well written and illustrated with many pretty color graphs and figures. Color adds a dimension in pattern recognition and the authors exploit it in this book. It is really the first of its kind that treats data mining from a statistical perspective and is so comprehensive and up-to-date.

The important statistical tools that are covered in this book include under the category of supervised learning; regression, discriminant analysis, kernel methods, model assessment and selection, bootstrapping, maximum likelihood and Bayesian inference, additive models, classification and regression trees, multivariate adaptive regression splines, boosting, regularization methods, nearest neighbor classification, k means clustering algorithms and neural networks. These methods are illustrated using real problems.

Similarly under the category of unsupervised learning, clustering and association are covered. They cover the latest developments in principal components and principal curves, multidimensional scaling, factor analysis and projection pursuit.

This book is innovative and fresh. It is an important contribution that will become a classic. The level is between intermediate and advanced. Good for an advanced special topics course for graduate students in statistics. A comparable text is the text by Mannila, Hand and Smyth.

This book made effective use of color and maintained a competitive price. This had a major impact on publishers like Wiley that could not sell a book at this size and initial price. Wiley is still looking for a book comparable to this one that they can use to compete with Springer-Verlag. I know this information because I heard from the Wiley acquisitions editor that I worked with on my two books.

Data mining is a field developed by computer scientists but many of its crucial elements are imbedded in important and subtle statistical concepts. Statisticians can play an important role in the development of this field but as was the case with artificial intelligence, expert systems and neural networks the statistical research community has been slow to respond. Hastie, Tibshirani and Friedman are changing this.
Friedman has been a major player in pattern recognition of high dimensional data, in tree classification, regularized discriminant analysis and multivariate adaptive regression splines. He has also done some exciting new research on boosting methods.

Hastie and Tibshirani invented additive models which are very general types of regression models. Tibshirani invented the lasso method and is a leader among the researchers on bootstrap. Hastie invented principal curves and surfaces.

These tools and the expertise of these authors make them naturals to contribute to advances in data mining. They come with great expertise and see data mining from the statistical perspective. They see it as part of a more general process of statistical learning from data.

The book is well written and illustrated with many pretty color graphs and figures. Color adds a dimension in pattern recognition and the authors exploit it in this book. It is really the first of its kind that treats data mining from a statistical perspective and is so comprehensive and up-to-date.

The important statistical tools that are covered in this book include under the category of supervised learning; regression, discriminant analysis, kernel methods, model assessment and selection, bootstrapping, maximum likelihood and Bayesian inference, additive models, classification and regression trees, multivariate adaptive regression splines, boosting, regularization methods, nearest neighbor classification, k means clustering algorithms and neural networks. These methods are illustrated using real problems.

Similarly under the category of unsupervised learning, clustering and association are covered. They cover the latest developments in principal components and principal curves, multidimensional scaling, factor analysis and projection pursuit.

This book is innovative and fresh. It is an important contribution that will become a classic. The level is between intermediate and advanced. Good for an advanced special topics course for graduate students in statistics. A comparable text is the text by Mannila, Hand and Smyth.

This book made effective use of color and maintained a competitive price. This had a major impact on publishers like Wiley that could not sell a book at this size and initial price. Wiley is still looking for a book comparable to this one that they can use to compete with Springer-Verlag. I know this information because I heard from the Wiley acquisitions editor that I worked with on my two books.

Data mining is a field developed by computer scientists but many of its crucial elements are imbedded in important and subtle statistical concepts. Statisticians can play an important role in the development of this field but as was the case with artificial intelligence, expert systems and neural networks the statistical research community has been slow to respond. Hastie, Tibshirani and Friedman are changing this.
Friedman has been a major player in pattern recognition of high dimensional data, in tree classification, regularized discriminant analysis and multivariate adaptive regression splines. He has also done some exciting new research on boosting methods.

Hastie and Tibshirani invented additive models which are very general types of regression models. Tibshirani invented the lasso method and is a leader among the researchers on bootstrap. Hastie invented principal curves and surfaces.

These tools and the expertise of these authors make them naturals to contribute to advances in data mining. They come with great expertise and see data mining from the statistical perspective. They see it as part of a more general process of statistical learning from data.

The book is well written and illustrated with many pretty color graphs and figures. Color adds a dimension in pattern recognition and the authors exploit it in this book. It is really the first of its kind that treats data mining from a statistical perspective and is so comprehensive and up-to-date.

The important statistical tools that are covered in this book include under the category of supervised learning; regression, discriminant analysis, kernel methods, model assessment and selection, bootstrapping, maximum likelihood and Bayesian inference, additive models, classification and regression trees, multivariate adaptive regression splines, boosting, regularization methods, nearest neighbor classification, k means clustering algorithms and neural networks. These methods are illustrated using real problems.

Similarly under the category of unsupervised learning, clustering and association are covered. They cover the latest developments in principal components and principal curves, multidimensional scaling, factor analysis and projection pursuit.

This book is innovative and fresh. It is an important contribution that will become a classic. The level is between intermediate and advanced. Good for an advanced special topics course for graduate students in statistics. The only comparable text is the text by Mannila, Hand and Smyth that I hope to be able to review in the near future.

i really like this book. i haven't finished reading yet. it's extremely dense. by that, i mean every page, every paragraph is packed full of information. it makes for slow but very rewarding reading. i bought the book because

i wanted to learn something about the topic. i've got a math and statistics background, but i haven't dealt with the broad topic of data mining or statistical learning. the book suits my needs very very well.

it's clearly written. i haven't found any grammatical or technical errors. it's pacing is ambitious, but i find i can follow it. i do think some math and statistics background is required to make the book readable and useful.

i wouldn't hesitate to recommend it to someone with the appropriate background.

This book describes most of the important topics in machine learning. Most machine learning books just present a criterion and and an optimization algorithm. For instance, LDA is often presented as: here is the Fisher criterion, it seems like a good thing to maximize. "The Elements of Statistical Learning" also presents that this is the right criterion if the distributions of the data for each class are Gaussian with the same covariance. This book puts all the algorithms in the same statistical language, which makes them easy to compare and choose between.

I also appreciate the emphasis this book puts on algorithms that are more recently popular/effective. I very much appreciate the discussions of logistic regression vs. LDA, ridge and lasso regression, boosting/additive logistic regression and additive trees, decision and regression trees, ...

The only qualm I have with this book is that it is rather biased toward the authors' own research. It is difficult from reading this book alone to differentiate between classical techniques and the authors' recent proposed algorithms.

I'm a machine learning person, and this book provides pretty thorough state-of-art and up-to-date (relatively well) summary of statistical methods being used in lots of pattern classification fields. One thing that does not exist in the book is generative models, although this book is the best of the kind that describes discriminitive models.

If you are looking for a relatively rigorous but very readable data mining book, this is simply the best! It covers most of the modern techniques and is beautifully printed with high quality graphics.

I'm not familiar with statistics but I currently need a good understanding of the topics covered in this book, so I bought it. I found that, to understand equations, I have to consult other statistics books or papers. Exerscise problems are more difficult than I expected, and again, I had to search the web (or articles) to get a hint to solve those. This may be because I don't have strong statistical background.

I'm learning a lot from this book but it is mainly because it requires me to study other references.

So, if you think you are in the beginner's level, don't expect that you can get enough information from this book to apply those methods to the real world. You will have to consult other references. Remember that each chapter can be a subject of a book, so it is compressed a lot. But, still, this is a good book because it gives you the direction and advice on each subject. You can start your work by reading appropriate portion of this book, then read other related books or papers. Treat this book as a stack of voice records of seminars by experts.

So, if you are an expert, I think this book can give you a good summary on each subject, as other reviewers, who are experts in this field, said (some experts said this is almost a Bible).

The discussed book is very explanatory and could be students' material for academic lessons.

This book has become a classic for any statistician and data miner by now.
It is a broad overview of regression, classification and clustering techniques (supervised and unsupervised machine learning).

Having to go thru some of the data mining such as CART, I find the book rather wordy. Sometimes, it takes a couple of readings to understand, too many termiologies. I think a lot of stuff might be better illustrated with mathematical formulae rather than words or both. For e.g., I was trying to understand what is surrogate variable, getting a big picture from the English is okay, but to actually compute it, I find information not as clear. This book is written in a way ... seem to be theoretical, and yet not rigorous enough.

A lot of examples, but I do hope it's not so long-winded in description.

Having to go thru some of the data mining such as CART, I find the book rather wordy. Sometimes, it takes a couple of readings to understand, too many termiologies. I think a lot of stuff might be better illustrated with mathematical formulae rather than words or both. For e.g., I was trying to understand what is surrogate variable, getting a big picture from the English is okay, but to actually compute it, I find information not as clear. This book is written in a way ... seem to be theoretical, and yet not rigorous enough.

A lot of examples, but I do hope it's not so long-winded in description.

This book is a very interesting book to learn the main statistical approach of data mining. It's clear and full of examples. If you go a Stanford data mining website you will find all the courses and exercises linked to the book.
An important book to have in your own data mining library.

This book is a very interesting book to learn the main statistical approach of data mining. It's clear and full of examples. If you go a Stanford data mining website you will find all the courses and exercises linked to the book.
An important book to have in your own data mining library.

Desde el punto de vista tecnico este libro es sumamente completo ya resume muy bien muchos de los metodos estadistico que se aplican en la mineria de datos. Pero a mi parecer este libro es debil en cuanto a la descripcion de ciertos algortimos que describe, y en algunos casos solo se menciona el procedimiento como una formula (la matriz S de los trazadores cubicos) ya que presupone que el lector ya tiene un buen conocimiento sobre trazadores cubicos.

Este no es un libro introductorio de mineria de datos, se tiene que tener un cierto nivel en analisis estadistico multivariado

Desde el punto de vista tecnico este libro es sumamente completo ya resume muy bien muchos de los metodos estadistico que se aplican en la mineria de datos. Pero a mi parecer este libro es debil en cuanto a la descripcion de ciertos algortimos que describe, y en algunos casos solo se menciona el procedimiento como una formula (la matriz S de los trazadores cubicos) ya que presupone que el lector ya tiene un buen conocimiento sobre trazadores cubicos.

Este no es un libro introductorio de mineria de datos, se tiene que tener un cierto nivel en analisis estadistico multivariado

These guys have made a great contribution to the statistical literature. It is a broad book that entends to summarize the latest methods available for data analysis. The authors succeed in giving a statistical context with which to compare and contrast many statistical methods. Some of the statistical methods discussed were developed in the past 5-15 years (SVM, boosting, LASSO, etc...) and haven't yet been put into a broader context. While this book is not comprehensive in its treatment, it is the best single book on data analysis available.

The authors of this book are certainly no nobodies in this area. However, this does not imply that they are able to write good books about statistical learning theory covering a broad range of methods.

In my opinion, the major problem of this book is, that it does not fascinate the reader. The opposite is almost true. One reviewer wrote, it is no book for beginners. Well, that is not my point. My point is, e.g., a chapter or section starts and the introduction provided to this topic under consideration is almost completely missing. Moreover, the explanations given in the main text are just not good. Sorry, it makes not much sense to collect and present a lot of deep results of scientific articles in statistical learning theory without the necessary explanations. One should not forget, each section corresponds roughly to one or even more articles. One would expect from the authors to provide a precise summary of the main points in an appealing way. Negative report! This is really sad, because, the colored illustrations provided in the book are just great.

Certainly, no bad book, because it provides definetely a quite good overview, but sadly not good to read and the explanations are not insightful.

Model averaging is an integral part of model selection and prediction.
Bayesian model averaging (BMA) is a highly sophisticated method for doing model averaging.
It is amazing then that the book hardly touches upon BMA or Bayesian methods.
(By my counting there is a total of 7 pages in all).

In my opinion the authors are very weak in this area which
explains why the topic of BMA is not covered. This is a
shame because it is more than likely BMA would be a serious
competitor (if not better) than the other methods they are
familiar with and discuss in the text.

The book is written by some of the biggest names currently in the field, and thus is written at a certain level, this isn't a fault of the book or the authers, but rather it was written for a specific audience. However I did find it odd when they would occassionally explain basic readily known notation, but later on assume the reader is familiar with what I would regard as advanced notation, or leave out quite a few steps in their mathematics assuming the reader understands what they did. This book covers a wide range of techniques ranging from the more traditional to the current, and for each topic presents an overview of the technique and provides adequate references for further exploration.

The reader should have a good underlying understanding of linear algebra, statistics and probability theory and also be familiar with the techniques presented here. This book was used in a graduate engineering data mining class, and most of us struggled greatly with the book. This book probably would have been more appropriate if this was a book to augment another text, or if this had not been the first time we had seen topics such as those presented, this being the book to explain neural networks, support vector machines and whatnot when you've never seen them before makes for a very bewildering experience, but once you find a few journal articles the techniques actually are fairly easy to understand.

The book does not explain how to implement using software any of the techniques, this is a topic left up to other books, such as Modern Applied Statistics with S by Ripley and Venerables, and only in their discussion about apriori for association rules did I see that they state a software package. It would have been nice if they would have given some insight into how they created some of the great graphics that punctuate the book, perhaps as additional material on the website.

A book that is more down to earth for engineers, albeit different in scope, would be Duda and Hart's Pattern Classification, which I believe are electrical engineers and written more from an engineering standpoint. In addition the Duda and Hard book gives a lot of applications-based problems and has an associated MATLAB handbook to walk readers through building many types of learners, while this book the end-of-chapter excercises are almost exclusively proofs and theoretical excercises. Not a fault of the book, but rather just a difference and depends on what the reader wants to get out of it.

Ultimately, even though it did prove to be a rather confusing book, I have learned a lot from it and will continue to go through it to learn even more from it as it does tend to become more lucid the more I go through it.

This is not an introduction to statistical learning theory. It is a collection of overviews of various statistical methods presented rather than explained to the reader. In order to benefit from this book the reader should have a good background in matrix algebra and should already have a theoretical and working knowledge of the topics covered. For detail on the methods and their real world application the reader should also be prepared to consult other references. Two stars because, fairly or not, it does not have the pedagogical value that I expected of it.

That's what happened to me respect this book. Conclusion: DO NOT BUY ANY BOOK, EVER, WITHOUT FLIPPING IT. OTHER'S REVIEW ARE NOT RELIABLE. This is the third time I bougth a book based on other's review. I repented.

This book is a miracle of clarity and comprehensiveness. It presents a unified approach to state of the art machine learning techniques from a statistical perspective. The layout is logical and the level of math is appropriate for applications-oriented engineers and computer scientists, as well as theorists. Sections where the book does need to go into heavier mathematics are clearly marked and generally optional. I found the book very easy to read, but at the same time very comprehensive.

The book provides a very illuminating counterpoint to other books that promote the Computational Learning Theory (COLT / kernels / large margins) viewpoint of modern machine learning. Many of the same techniques such as boosting and support vector machines are discussed, but are motivated in different ways. Appropriate regularization is seen as the key to avoiding overfitting with complex models, rather than margin maximization. Mathematically you often end up solving the same problem, but personally I usually find the statistical approach much more direct and intuitive.

This book is a nice follow on to introductory pattern recognition texts such as Duda and Hart, though it can be read without any prior pattern recognition knowledge. It provides a nice mix of theory and paractical algorithms, illustrated with numerous examples. An essential element of your machine learning library!

This book is a miracle of clarity and comprehensiveness. It presents a unified approach to state of the art machine learning techniques from a statistical perspective. The layout is logical and the level of math is appropriate for applications-oriented engineers and computer scientists, as well as theorists. Sections where the book does need to go into heavier mathematics are clearly marked and generally optional. I found the book very easy to read, but at the same time very comprehensive.

The book provides a very illuminating counterpoint to other books that promote the Computational Learning Theory (COLT / kernels / large margins) viewpoint of modern machine learning. Many of the same techniques such as boosting and support vector machines are discussed, but are motivated in different ways. Appropriate regularization is seen as the key to avoiding overfitting with complex models, rather than margin maximization. Mathematically you often end up solving the same problem, but personally I usually find the statistical approach much more direct and intuitive.

This book is a nice follow on to introductory pattern recognition texts such as Duda and Hart, though it can be read without any prior pattern recognition knowledge. It provides a nice mix of theory and paractical algorithms, illustrated with numerous examples. An essential element of your machine learning library!

I was already familiar with many of the topics covered in this book, but had to do a double take when reading about familiar concepts. Unfortunately, the authors' unique perspective is not presented in a way that is benificial to the reader. I would strongly suggest another book as a reference or introduction to this material.

The review from September 8 expresses an opinion which is the exact opposite of mine, and is worded so strongly that I have to object. I gave a course using the book to bioinformaticians, most of them with a computer science background, and found the book exceptionally well prepared and suitable for a graduate course. The book serves the dual purpose of an introduction and a reference. An especially nice feature is how the authors explain the relationships and differences between different methods. By doing so, they provide context which I have not seen in any other book on this subject. The book is a very nice combination of basic theory and performance evaluation on data from a wide variety of domains and it is quite up-to-date. It has a well developed website going with it and the graphical material can be obtained electronically from the publisher. The book is an outstanding contribution to the field.

This book is an excellent survey of the huge area of statistics / computer science called statistical learning. The discussion is interesting and accurate, but not too theoretical. It is the best book to date for a general audience with a reasonable math/stat background. One of the strengths is the wide variety of topics covered; it is very comprehensive. If there is a weakness, it is that depth is limited. Plenty of references are provided for further study, and the authors maintain a website. Recommended as a reference or a starting point for an applied statistician or mathematician, or as a text for a first course in the subject.

Among my commercial data mining friends this book is considered to be the bible. It is worth having just to assess the mindset of the day-to-day data miners. The book discusses many data mining issues in more depth than most of the earlier works on this subject. However it still lacks the the depth and counsel of, say, applied multiple regression books (cf. Draper and Smith) that give guidance on when a particular method may give false results or how bogus results can be detected posteriori.