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Book review of The Elements of Statistical Learning: Data Mining, Inference and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman; and Generalizability Theory, by Robert L. Brennan

There is much current research in the statistics community on developing and using algorithms for discovering knowledge and structure from vast amounts of data. Some researchers call this “learning from data” (e.g., Bradshaw & Boose, 1990). Others simply refer to it as “data mining” (e.g., Padmanabhan & Tuzhilin, 2002). Data mining is at the interface between statistics and information (computer) systems. It is an emerging field that has attracted much attention in a very short period of time. Data mining is applicable across a variety of disciplines and provides methodologies for such diverse problems as fraud detection, customer satisfaction, risk analysis, bankruptcy prediction, and credit scoring, to name a few. This book provides an excellent introduction to learning from data. It is written at a level that should appeal not only to statisticians but also to researchers and practitioners from a wide variety of fields. Although some mathematical details are provided, the emphasis tends to be more on the conceptual aspects of each method rather than their theoretical properties. There are also some very good example cases throughout the various chapters. The book concentrates mainly on methods that are considered supervised learning, but does offer one chapter on unsupervised learning. In general, supervised learning attempts to predict the value of an outcome variable using any number of input variables. Based on this definition, various types of discriminant and regression analyses approaches would be STRUCTURAL EQUATION MODELING, 11(1), 150–152 Copyright © 2004, Lawrence Erlbaum Associates, Inc.