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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

4.39  ·  Rating details ·  1,430 ratings  ·  45 reviews
During the past decade there has been an explosion in computation and information technology. With it has 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 ...more
Hardcover, 552 pages
Published September 2nd 2003 by Springer (first published January 1st 2001)
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 ·  1,430 ratings  ·  45 reviews

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Clif Davis
Feb 08, 2013 rated it it was amazing
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
Nov 20, 2015 rated it it was amazing
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.
Feb 23, 2008 rated it it was amazing
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
Amit Misra
Apr 24, 2018 rated it it was amazing  ·  review of another edition
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
Wooi Hen Yap
Jul 31, 2011 rated it really liked it  ·  review of another edition
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.
Rohit Goswami
Jun 27, 2020 rated it it was amazing
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.
Jason Yang
Oct 13, 2011 rated it it was amazing
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
Jul 18, 2015 rated it really liked it
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. ...more
Dan Boeriu
Jun 13, 2017 rated it it was ok
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.
May 28, 2017 rated it liked it
Shelves: machine-learning
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.
Terran M
Nov 21, 2018 rated it really liked it  ·  review of another edition
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 ...more
Dec 31, 2018 rated it liked it
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
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
Miguel Martins
Nov 04, 2019 rated it it was amazing
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.
Jin Shusong
Jun 15, 2017 rated it it was amazing  ·  review of another edition
Everyone in machine learning area should read it.
Dec 17, 2019 rated it it was amazing
Amazing read for anyone who is interested in Data Science. The chapters are all very well written.

Apr 17, 2020 rated it it was amazing
Shelves: stanford
Rigorous and mathematically dense books for machine learning. One of the most challenging books I’ve ever read.
Gregory Reshetniak
May 05, 2020 rated it it was amazing
Best book on data science ever.
May 15, 2020 rated it really liked it
Shelves: shelved
good reference text. Selective reading.
May 20, 2020 rated it it was amazing
Shelves: half-read
read another book
sarah chang
Jun 29, 2020 rated it really liked it
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.
Razvan Coca
Nov 19, 2016 rated it it was amazing
It sounds like the right perspective on Machine Leaning
Terran M
May 19, 2018 rated it really liked it  ·  review of another edition
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 ...more
Aug 13, 2007 rated it really liked it
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.
The topics are described more from a statistics perspective than the computer science perspective, but as written by statisticians for computer scientists instead of for other statisticians. The examples are interesting and the graphics very nice.
Kyle Hart
Apr 27, 2014 rated it it was amazing
The best explanation of additive models and piecewise approaches to linear modeling that I've found. The section on b-splines is especially good. The book is a treasure--one of my favorites. Worth keeping on the shelf to go back to over and over.
Jun 04, 2014 rated it it was amazing
Shelves: scott, owned
A classic! One of the first books I read on Machine Learning. Comes at things from the statistics perspective, probably wouldn't recommend as a first introduction. Also would recommend the updated electronic editions (freely available form Hastie's webpage
Tim Shi
Jan 05, 2015 rated it it was amazing
This book shows a nice statistical foundation of modern machine learning. Due to the rapid development of this field, the book seemed a bit out-dated. The notations are sometimes messed up too. But anyway, it reveals a unique statistical perspective of learning and is quite interesting per se.
Mar 30, 2010 rated it really liked it
One of the more useful stat machine learning books I've read. Correct authors are Hastie, Friedman, and Tibshirani.
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