Springer Series in Statistics
Trevor Hastie
Robert Tibshirani
Jerome Friedman
Springer Series in Statistics
The Elements of
Statistical Learning
Data Mining,Inference,and Prediction
The Elements of Statistical Learning
During the past decade there has been an explosion in computation and information tech-
nology. With it have come vast amounts of data in a variety of fields such as medicine, biolo-
gy, finance, and marketing. The challenge of understanding these data has led to the devel-
opment 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 should be 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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springer.com
STATISTICS
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Trevor Hastie • Robert Tibshirani • Jerome Friedman
The Elements of Statictical Learning
Hastie • Tibshirani • Friedman
Second Edition
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