Amazon.com: Customer reviews: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Top positive review

59 people found this helpful

Actually does something (huge) with the math

ByJohn Mounton May 17, 2014

I have been using The Elements of Statistical Learning for years, so it is finally time to try and review it.

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).

Top critical review

See all 33 critical reviews›

46 people found this helpful

A frustrating experience

ByA. Perrieron December 15, 2015

I was happy to discover that I was not the only one struggling with this book.
I have a PhD in Math and more particularly stochastic processes and everytime I open this book I can't quite understand what the content is.
I've read other Machine Learning books with the same level of math and abstraction and did not encounter this difficulty. 'Boosting' by Schapire and Freund for instance.
The flow of the text is hard to follow. A lot of Jargon is used assuming the reader is already familiar with the notions exposed.
I think I will end up making sense of the text only after I've been working in the field for many years.

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Showing 21-30 of 85 reviews(Verified Purchases). See all 130 reviews

Five Stars

ByAmazon Customeron March 1, 2017

Format: HardcoverVerified Purchase

Must have

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Five Stars

Bywenjie c.on October 17, 2016

Format: HardcoverVerified Purchase

very good!

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Excellent but assumes considerable background

ByM. D. HEALYon January 22, 2010

Format: HardcoverVerified Purchase

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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Five Stars

ByWentao Geon June 6, 2016

Format: HardcoverVerified Purchase

Good

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Five Stars

ByAaronon January 1, 2017

Format: HardcoverVerified Purchase

Great book for stats student. Covers basic machine learning techniques.

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You can download this book for free online.

Byabkon September 7, 2015

Format: HardcoverVerified Purchase

Good book. Download it. Don't pay for it.

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Five Stars

ByJayon July 25, 2016

Format: HardcoverVerified Purchase

great book.

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I imagine anyone not especially interested in the theory of ...

ByDodge Coateson May 18, 2017

Format: HardcoverVerified Purchase

I imagine anyone not especially interested in the theory of machine learning will be frustrated and turned off by this book. For everyone else, buckle up

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