The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) (Hardcover)

~ (Author), (Author), Jerome Friedman (Author)

Editorial Reviews

Review

From the Reviews:

"Like the first edition, the current one is a welcome edition to researchers and academicians equally…. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!" (Book Review Editor, Technometrics, August 2009, VOL. 51, NO. 3)

Product Description

During the past decade there has been an explosion in computation and information technology. With it have 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, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

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Product Details

• Hardcover: 746 pages
• Publisher: Springer; 2nd ed. 2009. Corr. 3rd printing edition (September 15, 2009)
• Language: English
• ISBN-10: 0387848576
• ISBN-13: 978-0387848570
• Product Dimensions: 9 x 6.3 x 1.6 inches
• Shipping Weight: 3.1 pounds (View shipping rates and policies)
• Average Customer Review: 3.8 out of 5 stars  See all reviews (33 customer reviews)
• Amazon.com Sales Rank: #6,450 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

155 of 169 people found the following review helpful:

5.0 out of 5 stars data mining through the eyes of statisticians, October 1, 2001

By	Michael R. Chernick "statman13" (Malvern, PA) - See all my reviews (REAL NAME)

This review is from: The Elements of Statistical Learning (Hardcover)

Data mining is a field developed by computer scientists but many of its crucial elements are imbedded in important and subtle statistical concepts. Statisticians can play an important role in the development of this field but as was the case with artificial intelligence, expert systems and neural networks the statistical research community has been slow to respond. Hastie, Tibshirani and Friedman are changing this.

Friedman has been a major player in pattern recognition of high dimensional data, in tree classification, regularized discriminant analysis and multivariate adaptive regression splines. He has also done some exciting new research on boosting methods.

Hastie and Tibshirani invented additive models which are very general types of regression models. Tibshirani invented the lasso method and is a leader among the researchers on bootstrap. Hastie invented principal curves and surfaces.

These tools and the expertise of these authors make them naturals to contribute to advances in data mining. They come with great expertise and see data mining from the statistical perspective. They see it as part of a more general process of statistical learning from data.

The book is well written and illustrated with many pretty color graphs and figures. Color adds a dimension in pattern recognition and the authors exploit it in this book. It is really the first of its kind that treats data mining from a statistical perspective and is so comprehensive and up-to-date.

The important statistical tools that are covered in this book include under the category of supervised learning; regression, discriminant analysis, kernel methods, model assessment and selection, bootstrapping, maximum likelihood and Bayesian inference, additive models, classification and regression trees, multivariate adaptive regression splines, boosting, regularization methods, nearest neighbor classification, k means clustering algorithms and neural networks. These methods are illustrated using real problems.

Similarly under the category of unsupervised learning, clustering and association are covered. They cover the latest developments in principal components and principal curves, multidimensional scaling, factor analysis and projection pursuit.

This book is innovative and fresh. It is an important contribution that will become a classic. The level is between intermediate and advanced. Good for an advanced special topics course for graduate students in statistics. The only comparable text is the text by Mannila, Hand and Smyth that I hope to be able to review in the near future.

87 of 93 people found the following review helpful:

5.0 out of 5 stars Useful book on data mining, February 6, 2002

By	frank lindemann - See all my reviews (REAL NAME)

Amazon Verified Purchase

This review is from: The Elements of Statistical Learning (Hardcover)

I use data mining tools in my financial engineering and financial modeling work and I have found this book to be very useful. This book provides two crucial types of information. First, it provides enough theory to allow a potential user to understand the essential insights that motivate specific techniques and to evaluate the situations in which those technique are appropriate. Second, the book gives the exact algorithms to implement the various techniques.
While no book I have seen covers every data mining methodology available, this one has the strongest coverage I have seen in additive models, non-linear regression, and CART/MART (regression/classification trees). It also has very strong coverage in many other areas. I highly recommend it.

49 of 51 people found the following review helpful:

4.0 out of 5 stars The Elements of Statistical Learning, December 18, 2001

By A Customer

This review is from: The Elements of Statistical Learning (Hardcover)

The book by Hastie, Tibshirani and Friedman is a welcome
addition to the quickly growing area of machine learning
and data mining. This is a well written book, laid out
nicely with excellent examples by 3 well established
researchers in the field. It will be helpful to those
who are interested in learning about this field, as well
as experts who want to know more

My only complaint is that although the authors do
make an honest attempt to clearly highlight methods
that are based on their own research,
often this distinction becomes cloudy and the reader
is left with the impression that the methods
advocated are often the best and represent
the standard in the industry. In fact many of
their ideas are only heuristic and it is more than
conceivable that these will eventually be superseeded
with better methods.

A good book, which gets you up to speed in the literature
but it will only be relevant for a few years.