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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
by
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour graphics. It is a valuable resource for statisticians and anyone interested in data mi
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Kindle Edition, 745 pages
Published
May 3rd 2018
(first published January 1st 2001)
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Start your review of The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Excellent book. Has repaid multiple rereadings and is a wonderful springboard for developing your own ideas in the area. Currently I'm going through Additive Models again which I breezed by the first few times. The short section on the interplay between Bias, Variance and Model Complexity is one of the best explanations I've seen.
After retiring, I developed a method of learning a variation of regression trees that use a linear separation at the decision points and a linear model at the leaf nod ...more
After retiring, I developed a method of learning a variation of regression trees that use a linear separation at the decision points and a linear model at the leaf nod ...more
Well, it was one of the most channeling books I've read in my career. It is a rigorous and mathematically dense book on machine learning techniques.
Be sure to refine your understanding of linear algebra and convex optimization before reading this book. Nonetheless, the investment will totally worth it. ...more
Be sure to refine your understanding of linear algebra and convex optimization before reading this book. Nonetheless, the investment will totally worth it. ...more
Feb 23, 2008
Amir-massoud
rated it
it was amazing
·
review of another edition
Recommends it for:
anyone who is interested in machine learning
Recommended to Amir-massoud by:
Dale Schuurmans
Shelves:
machine-learning
This book surveys many modern machine learning tools ranging from generalized linear models to SVM, boosting, different types of trees, etc.
The presentation is more or less mathematical, but the book does not provide a deep analysis of why a specific method works. Instead, it gives you some intuition about what a method is trying to do. And this is the reason I like this book so much. Without going into mathematical details, it summarizes all necessary (and really important) things you need to ...more
The presentation is more or less mathematical, but the book does not provide a deep analysis of why a specific method works. Instead, it gives you some intuition about what a method is trying to do. And this is the reason I like this book so much. Without going into mathematical details, it summarizes all necessary (and really important) things you need to ...more
I read this book for work, during work, but I'm falling behind my yearly goal so I'm including it on goodreads :P
This book has a lot in it, and is incredibly dense. However, it's well worth it. It contains not quite everything about statistics and machine learning that someone needs to know to do data science, but it comes close.
The drawback is that this book is hard to understand. You need to know a lot, or be willing to learn a lot from other resources, to actually get a lot from this book. E ...more
This book has a lot in it, and is incredibly dense. However, it's well worth it. It contains not quite everything about statistics and machine learning that someone needs to know to do data science, but it comes close.
The drawback is that this book is hard to understand. You need to know a lot, or be willing to learn a lot from other resources, to actually get a lot from this book. E ...more
A classic text in machine learning from statistical perspective. No matter you're a novice machine learning practitioner, undergrad or hardcore PhD you can't miss out on this one. Overall, a good nontrivial broad intro to machine learning without loss of technical depth.
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A more detailed companion piece to the introductory ISLR, this is an excellent introduction. The only critique would be that, it is too even-handed to influence the mindset of the reader much.
An extremely well-written introduction to machine learning. I now understand why this is the universal textbook for machine learning classes.
The math is described at a reasonably high level, but the authors do a fantastic job emphasizing the conceptual differences between different learning algorithms. A major focus of this text is on conditions which favor some algorithms over others in minimizing variability for different learning exercises. While this book is not a very pragmatic text (does n ...more
The math is described at a reasonably high level, but the authors do a fantastic job emphasizing the conceptual differences between different learning algorithms. A major focus of this text is on conditions which favor some algorithms over others in minimizing variability for different learning exercises. While this book is not a very pragmatic text (does n ...more
It's a classic, but it's not my favorite text at this level for either teaching or self-study. Coverage of core methods is relatively good, but the content sometimes veres between highly mathematical and formulaic, missing important conceptual areas. I wouldn't consider a statistics/ML/bioinformatics/... library complete without ESL, but I think Pattern Recognition and Machine Learning is a better overall resource and aid to teaching this content.
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For the mathematician - this book is too terse and hard to learn from to the point of pretentiousness.
For the software engineer - the algorithms presentation in this book is poor. A bunch of phrases with no clear state change, step computations, etc.
In general - a lot of pompous presentations and hand waiving material.
Something positive: the paper is top quality.
For the software engineer - the algorithms presentation in this book is poor. A bunch of phrases with no clear state change, step computations, etc.
In general - a lot of pompous presentations and hand waiving material.
Something positive: the paper is top quality.
Nice as a reference or an overview, but not necessarily as a source for learning. So many approaches and techniques are described in this book, that out of necessity, their description is very general, very condensed and very mathematical.
This is an excellent second or third book on statistical modeling, after you have read something with code examples and done a few real projects. It is mathematically deeper and more comprehensive than An Introduction to Statistical Learning: With Applications in R and does more to tie together how and why algorithms work. It provides no code examples, and it is also correspondingly more demanding in the mathematical background of the reader. Even if you never read all of it, it's worthwhile own
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This book has been the referential authority for current users of supervised and unsupervised ML. Having already an econometrics and probability background, this book was quite accessible and enjoyable to read. I appreciate the methodical and careful style, though at times it feels terse. I guess the reason is that the book is already quite long and is not meant to be a deep dive into methodology or theory. That said, the book is very good as an introduction and a reference to ML methods. I thin
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it's the classic for good reason, well written and well organized, but this field is not as magical as people believe. And decorating machine-learning books with informative, colorful, frequent pictures is absolutely what mathematical educators everywhere should be doing, but unfortunately it's only the intellectually vacuous computer fields that ever seem to stick enough pretty pictures in their books.
I would like to say machine learning won't make you the money you think it will, but sadly it ...more
I would like to say machine learning won't make you the money you think it will, but sadly it ...more
Aug 31, 2019
Chris
rated it
liked it
·
review of another edition
Shelves:
statistics,
machine-learning
Plenty of pictures. But the field is bullshit. Picture-heavy books like this are wonderful _except_ that then hundreds of pages are spend making it look like a thing which shouldn’t actually be considered a thing, is actually a thing.
It’s far better laid out than stuffy academic journal articles, yet as irrelevant as a stuffy academic journal.
Buy and read this if you’re a math student and want some pictures and examples of "how polynomials might apply to the real world". Buy and read it if you’r ...more
It’s far better laid out than stuffy academic journal articles, yet as irrelevant as a stuffy academic journal.
Buy and read this if you’re a math student and want some pictures and examples of "how polynomials might apply to the real world". Buy and read it if you’r ...more
Although covering wide range of topics, the book, especially towards the end, reads as a thick overview article, rather than a textbook. Yes, there're many problems to work on at the end of any chapter, but most concepts, ideas and algorithms presented would require the reader to refer to "original papers" if he attempts to implement them in computer code. So, while theoretically informative, the book is seriously lacking on practical level. More of a review than a reference.
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Nov 04, 2019
Miguel Martins
rated it
it was amazing
·
review of another edition
Shelves:
data-science
A clear and not-so-heavy on the math side introduction to Data Science and Statistical Learning.
I did not finish the book on its entirety since I already was versed in some of the topics. Notwithstanding, even in such situations, a quick glance gave me more intuition and nuance regarding to what I already knew.
I also learned a lot of new concepts, every Data Scientist should read this book.
I did not finish the book on its entirety since I already was versed in some of the topics. Notwithstanding, even in such situations, a quick glance gave me more intuition and nuance regarding to what I already knew.
I also learned a lot of new concepts, every Data Scientist should read this book.
I love this book. It’s been my constant fallback last couple of years. Whenever a question sprung up in my head about the fundamentals of an algorithm, ESL was there with just the precise, succinct information I needed. I normally don’t write reviews for textbooks, but this one had to be done. I owe one to ESL.
Everyone in machine learning area should read it.
Amazing read for anyone who is interested in Data Science. The chapters are all very well written.
Rigorous and mathematically dense books for machine learning. One of the most challenging books I’ve ever read.
Best book on data science ever.
good reference text. Selective reading.
read another book
A very comprehensive book on machine learning, but not much content on deep learning. Still worth a lot to be a reference book as the Bible of machine learning.
will read and study this again.
It sounds like the right perspective on Machine Leaning
This book covers most of the topics in statistical learning, but it is written in a way that is too terse.
Note that somehow the Kindle Edition is not associated with all the other editions of this book in the GoodReads database. See the rest of them at Elements of Statistical Learning
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Aug 13, 2007
David
rated it
really liked it
·
review of another edition
Recommends it for:
scientists and engineers
Great book covering the principles of applied statistical learning. The book's mathematical rigor is semi-formal, opting for intuitive explanations and keeping proofs to a minimum. Chapters contain a thorough treatment of their subject, touching on modern research topics.
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