The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition

This book presents key concepts across various fields—including medicine, biology, finance, and marketing—within a unified conceptual framework. Although it uses a statistical approach, the focus remains on intuitive understanding rather than complex mathematics. Richly illustrated with color graphics and numerous real-world examples, it serves as a valuable reference for both statisticians and those engaged in data mining across scientific and industrial domains.

Its wide-ranging coverage spans both supervised learning (prediction) and unsupervised learning, exploring topics such as neural networks, support vector machines, classification trees, and boosting—the first book to offer an in-depth treatment of the latter.

This major new edition introduces several topics not found in the original, including graphical models, random forests, ensemble methods, least angle regression and lasso path algorithms, non-negative matrix factorization, and spectral clustering. It also includes a dedicated chapter on techniques for handling "wide" data (where the number of variables exceeds the number of observations), addressing issues like multiple testing and false discovery rates.