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

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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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## Community Reviews

Showing 1-30

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

Download PDF at http://www-stat.stanford.edu/~tibs/El...
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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

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

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

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

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.

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

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.

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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