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Product details
File Size: 29278 KB
Print Length: 745 pages
Publisher: Springer; 2 edition (August 26, 2009)
Publication Date: August 26, 2009
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00475AS2E
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Amazon Best Sellers Rank:
#232,195 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
Very comprehensive, sufficiently technical to get most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book).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.
Math books, at least data science texts, can usually be divided into those which are easy to read but contain little technical rigor and those which are written with a scientific approach to methodology but are so equation dense that it’s hard to imagine them being read outside an advanced academic setting.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 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).
My background in statistics, statistical mechanics, and stochastic theory is old, but I'm not a zero at it. This is an unfriendly book. Some of the derivations are things you would see on the blackboard of an advanced course in statistics, not machine learning, and take careful notes of. I emailed one one of the authors for suggestions of a companion text, but received no reply.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.
If you don't have a background in math or statistics, I would recommend "An Introduction to Statistical Learning: With Applications in R" instead, but if you want a more rigorous book on machine learning, this is the the book for you.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.
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