An Introduction to Statistical Learning

这不是一本教你推导公式的书，而是一本教你在拿到一堆数据之后，先想清楚该问什么问题、再决定用什么方法的入门指南。全书以“预测准确度与模型可解释性如何取舍”“偏差与方差的权衡”“Free Lunch究竟意味着什么”为脉络，把回归、分类、重采样、收缩法、树模型、支持向量机、聚类等常用技术串成一条可操作的决策链。它的特别之处在于几乎不强调数学证明，却处处在训练读者的判断力：面对新数据，先问能否预测、由谁预测、预测多准、该如何预测。书末每章附带的R语言实验，把方法落到可运行的代码上，但整体仍重思路、轻工程。适合被《统计学习导论》的数学细节劝退、又渴望真正理解机器学习为何如此运作的读者。

Gareth James is a professor of data sciences and operations at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area. Daniela Witten is an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. 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.