An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

本书是引导人们对统计问题进行思考的一本杰出教材，是迄今为止我们所见到的最出色的关于统计思想的入门书。它以清晰的日常语言和各种类型的例子向人们介绍解决统计问题的若干技巧，展示了统计在诸如经济、教育、遗传、医药、物理、化学、环境污染、政治及社会科学、心理学等方面所起的至关重要的作用。在整个展开过程中除了加减乘除等简单运算之外几乎不用什么数学语言，这为各行各业有兴趣接受统计思想的人创造了极大的方便。