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To our parents:
Valerie and Patrick Hastie
Vera and Sami Tibshirani
Florence and Harry Friedman
and to our families:
Samantha, Timothy, and Lynda
Charlie, Ryan, Julie, and Cheryl
Melanie, Dora, Monika, and Ildiko
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Preface to the Second Edition
In God we trust, all others bring data.
–William Edwards Deming (1900-1993)
1
We have been gratified by the popularity of the first edition of The
Elements of Statistical Learning. This, along with the fast pace of research
in the statistical learning field, motivated us to update our book with a
second edition.
We have added four new chapters and updated some of the existing
chapters. Because many readers are familiar with the layout of the first
edition, we have tried to change it as little as possible. Here is a summary
of the main changes:
1
On the Web, this quote has been widely attributed to both Deming and Robert W.
Hayden; however Professor Hayden told us that he can claim no credit for this quote,
and ironically we could find no “data” confirming that Deming actually said this.
viii Preface to the Second Edition
Chapter What’s new
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression LAR algorithm and generalizations
of the lasso
4. Linear Methods for Classification Lasso path for logistic regression
5. Basis Expansions and Regulariza-
tion
Additional illustrations of RKHS
6. Kernel Smoothing Methods
7. Model Assessment and Selection Strengths and pitfalls of cross-
validation
8. Model Inference and Averaging
9. Additive Models, Trees, and
Related Methods
10. Boosting and Additive Trees New example from ecology; some
material split off to Chapter 16.
11. Neural Networks Bayesian neural nets and the NIPS
2003 challenge
12. Support Vector Machines and
Flexible Discriminants
Path algorithm for SVM classifier
13. Prototype Methods and
Nearest-Neighbors
14. Unsupervised Learning Spectral clustering, kernel PCA,
sparse PCA, non-negative matrix
factorization archetypal analysis,
nonlinear dimension reduction,
Google page rank algorithm, a
direct approach to ICA
15. Random Forests New
16. Ensemble Learning New
17. Undirected Graphical Models New
18. High-Dimensional Problems New
Some further notes:
• Our first edition was unfriendly to colorblind readers; in particular,
we tended to favor red/green contrasts which are particularly trou-
blesome. We have changed the color palette in this edition to a large
extent, replacing the above with an orange/blue contrast.
• We have changed the name of Chapter 6 from “Kernel Methods” to
“Kernel Smoothing Methods”, to avoid confusion with the machine-
learning kernel method that is discussed in the context of support vec-
tor machines (Chapter 11) and more generally in Chapters 5 and 14.
• In the first edition, the discussion of error-rate estimation in Chap-
ter 7 was sloppy, as we did not clearly differentiate the notions of
conditional error rates (conditional on the training set) and uncondi-
tional rates. We have fixed this in the new edition.
Preface to the Second Edition ix
• Chapters 15 and 16 follow naturally from Chapter 10, and the chap-
ters are probably best read in that order.
• In Chapter 17, we have not attempted a comprehensive treatment
of graphical models, and discuss only undirected models and some
new methods for their estimation. Due to a lack of space, we have
specifically omitted coverage of directed graphical models.
• Chapter 18 explores the “p À N” problem, which is learning in high-
dimensional feature spaces. These problems arise in many areas, in-
cluding genomic and proteomic studies, and document classification.
We thank the many readers who have found the (too numerous) errors in
the first edition. We apologize for those and have done our best to avoid er-
rors in this new edition. We thank Mark Segal, Bala Rajaratnam, and Larry
Wasserman for comments on some of the new chapters, and many Stanford
graduate and post-doctoral students who offered comments, in particular
Mohammed AlQuraishi, John Boik, Holger Hoefling, Arian Maleki, Donal
McMahon, Saharon Rosset, Babak Shababa, Daniela Witten, Ji Zhu and
Hui Zou. We thank John Kimmel for his patience in guiding us through this
new edition. RT dedicates this edition to the memory of Anna McPhee.
Trevor Hastie
Robert Tibshirani
Jerome Friedman
Stanford, California
August 2008