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

This book presents a comprehensive overview of key concepts in statistical learning, applying them to diverse fields such as medicine, biology, finance, and marketing. It establishes a common conceptual framework for these disciplines, prioritizing understanding over complex mathematics. The text covers a broad spectrum of methodologies, ranging from supervised learning techniques like prediction to various forms of unsupervised learning. It serves as a valuable resource for statisticians and professionals involved in data mining across both scientific research and industrial applications. The content includes detailed discussions on neural networks, support vector machines, classification trees, and boosting, marking the first comprehensive treatment of boosting in book form. This major new edition significantly expands the original scope by introducing advanced topics such as graphical models, random forests, and ensemble methods. Readers will also find in-depth coverage of least angle regression, path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. Furthermore, the book addresses modern challenges in data analysis, specifically focusing on methods for "wide" data where the number of predictors exceeds the number of samples. This includes a dedicated chapter on multiple testing and false discovery rates. To aid comprehension, the text is illustrated with numerous examples and a liberal use of color graphics, making complex statistical ideas accessible to a wider audience interested in the practical application of data science.