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.
1 Introduction 1
2 Overview of Supervised Learning 9
3 Linear Methods for Regression 43
4 Linear Methods for Classification 101
5 Basis Expansions and Regularization 139
6 Kernel Smoothing Methods 191
7 Model Assessment and Selection 219
8 Model Inference and Averaging 261
9 Additive Models, Trees, and Related Methods 295
10 Boosting and Additive Trees 337
11 Neural Networks 389
12 Support Vector Machines and Flexible Discriminants 417
13 Prototype Methods and Nearest-Neighbors 459
14 Unsupervised Learning 485
15 Random Forests 587
16 Ensemble Learning 605
17 Undirected Graphical Models 625
18 High-Dimensional Problems: p >> N 649
References 699
Author Index 729
