统计学习方法、数据挖掘、推断和概率模型等相关内容。

简介：
The core components of statistical learning, encompassing data mining, reasoning, and prediction, are presented here. A special acknowledgment is extended to our parents, Valerie and Patrick Hastie, and Vera and Sami Tibshirani, alongside our families: Samantha, Timothy, and Lynda; Charlie, Ryan, Julie, and Cheryl; Melanie, Dora, Monika, and Ildiko. This publication’s creation is deeply indebted to the support of Florence and Harry Friedman. Furthermore, we express our gratitude to those whose names are listed. A notable adage serves as inspiration for this work: “In God we trust, all others bring data” – William Edwards Deming (1900-1993). We are pleased to note the considerable interest in the initial edition of *The Elements of Statistical Learning*, a factor that spurred us to revise and expand upon its content with this second edition. To accommodate readers familiar with the structure of the previous version, we have maintained a largely consistent format while incorporating four new chapters and refining several existing ones. A concise overview of the principal modifications follows: 1. Exploring the concept of data-driven learning; 2. A comprehensive survey of supervised learning techniques; 3. Examination of linear methodologies for regression analysis utilizing the LASSO algorithm and its extensions; 4. Investigation into linear methods for classification through the lasso path for logistic regression; 5. Exploration of basis expansions and regularization within Reproducing Kernel Hilbert Spaces (RKHS); 6. Further visual representations illustrating RKHS concepts.