文件名称：An Introduction to Statistical Learning with Application in R (1)
文件大小：7.27MB
文件格式：PDF
更新时间：2021-10-11 06:26:04
R语言
Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics, and blends with parallel developments in computer science, and in particular machine learning. The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. With the explosion of “Big Data” problems statistical learning has be- come a very hot field in many scientific areas as well as marketing, finance and other business disciplines. People with statistical learning skills are in high demand. One of the first books in this area — The Elements of Statistical Learn- ing (ESL) (Hastie, Tibshirani, and Friedman) — was published in 2001, with a second edition in 2009. ESL has become a popular text not only in statistics but also in related fields. One of the reasons for ESL’s popu- larity is its relatively accessible style. But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics. In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details. We have created labs illustrating how to implement each of the statistical learning methods using the popular statistical software package R . These labs provide the reader with valuable hands-on experience. This book is appropriate for advanced undergraduates or master’s stu- dents in Statistics or related quantitative fields, or for individuals in other disciplines who wish to use statistical learning tools to analyze their data. It can be used as a textbook for a course spanning one or two semesters. We would like to thank several readers for valuable comments on prelim- inary drafts of this book: Pallavi Basu, Alexandra Chouldechova, Patrick Danaher, Will Fithian, Luella Fu, Sam Gross, Max Grazier G’Sell, Court- ney Paulson, Xinghao Qiao, Elisa Sheng, Noah Simon, Kean Ming Tan, Xin Lu Tan. It’s tough to make predictions, especially about the future. -Yogi Berra