- Series: Springer Series in Statistics
- Hardcover: 745 pages
- Publisher: Springer; 2nd edition (2016)
- Language: English
- ISBN-10: 0387848576
- ISBN-13: 978-0387848570
- Product Dimensions: 9.3 x 6 x 1.4 inches
- Shipping Weight: 2.6 pounds (View shipping rates and policies)
- Average Customer Review: 143 customer reviews
- Amazon Best Sellers Rank: #14,756 in Books (See Top 100 in Books)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition
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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.
143 customer reviews
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The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique).
Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated proofs.
I own two copies of this edition, one for the office, one for my house, and the authors generously provide the PDF for travelers like me.
The tragedy is that the material is well selected, and obviously essential for work in the field. It is just very poorly supported by off-handed, sketchy derivations that resemble inside jokes more than explanations.
The book brags of color illustrations. I would have preferred didactic coherence.
Download the free pdf from the Stanford site. If you find a companion text, only then buy the hard copy.
Fortunately, The Elements of Statistical Learning proves the exception. The text is full with the equations necessary to root the methodology without engaging the reader with long proofs that would tax those of us employing these techniques in the business world.
The visual aspects of the text seem to have been written with John Tukey or Edward Tufte in mind. Though their frequent use makes the book some seven hundred pages long, reading and comprehension is made much easier.
And, though it’s been almost ten years since the book was published, the techniques described remain, for the most part, at the cutting edge of data science.
I was told by some other analysts I know that this was their bible for data science. I was somewhat skeptical of this kind of hyperbole but was pleasantly surprised that the book matched these high expectations. If you have an undergraduate degree in a mathematically related discipline, The Elements of Statistical Learning will prove to be an invaluable reference to understand the rapidly advancing avalanche of data mining techniques.
I have since discovered textbooks that, although I haven't had the time to read them completely, in my opinion, serve as better resources for the material in this textbook, as well as prerequisite material.
For prerequisite material, I mentioned Plane Answers to Complex Questions by Christensen. Since I wrote my last review, I have also discovered A Primer on Linear Models by Monahan, as well as Foundations of Linear and Generalized Linear Models by Agresti. I've heard that the classic text on Linear Models by Searle is amazing, but have not had time to read it.
For alternatives to Elements of Statistical Learning, my #1 choice by far are the texts by Theodoridis, namely Machine Learning, and Pattern Recognition. These texts are huge and give a very realistic idea of the background it would take to learn this material. For something which isn't quite as dense but could serve as an alternative to Elements of Statistical Learning, I prefer Izenman's Modern Multivariate Statistical Techniques, which covers a lot of the same material. It has its own problems as well, but at the very least, Izenman goes out of his way in his text to define the notation that is used precisely over a chapter or two.
The statement of this textbook "expect[ing] that the reader will have had at least one elementary course in statistics, covering basic topics including linear regression." still bothers me to this day.
This book is a pedagogical disaster. It is essentially a bunch of topics lumped into one book, with unclear explanations and notation.
I am speaking particularly to the linear regression section. I took two linear models courses in a graduate-level statistics program, and I can say I finally understand the material in here. But the explanations are extremely poor. Note I didn't say poor - but beyond poor. The authors attest that they "expect that the reader will have had at least one elementary course in statistics, covering basic topics including linear regression." This seems to suggest that any student who has taken freshman-level/AP-level statistics can handle this material.
Yes, I agree with everyone, you need linear algebra and some mathematical maturity. But not only do you need linear algebra and mathematical maturity, you need much more than that: you need a substantial background in linear models, to even understand how in the world you find a variance-covariance matrix or expected value or a random vector, to understand why N-p-1 is the degrees of freedom for the unbiased estimator of the variance, to understand how in the world one defines differentiating with respect to the beta vector, and perhaps most importantly, to understand that the estimate of the response vector is H * y, H denoting the projection matrix onto the column space of X or the hat matrix.
Some may argue, this book isn't about the "how" and that I'm arguing too much about the theory, and that this is more of an "applied" book. Simply put, no matter what math or stats book I read, if I can't figure out the results that come out of a book using what is in the text itself and whatever prerequisites are suggested by the text, then it's a crappy textbook. You can't read this book with an elementary statistics background. Furthermore, I don't know anyone with only an elementary statistics background who could understand taking derivatives with respect to a vector.
I don't recommend this book unless you have an excellent grasp in linear model theory. Introduction to Statistical Learning, contrary to popular belief, is not a good prerequisite for this book. I recommend reading Plane Answers to Complex Questions by Christensen, or any similar book, before reading this text.
In addition the bound copy, I have the PDF from Hastie's site on my kindle, but for a reference as good as this one, having the bound version is absolutely worth the money. When I'm reading a math text, I tend to have one finger in a previous section, so I can refer back to definitions when I need to, and one finger in the index, which just isn't possible with the PDF / Kindle edition.