李欣海：用R实现随机森林的分类与回归.pdf

第五届中国R语言会议 北京2012 李欣海 用R实现随机森林的分类与回归 Applications of Random Forest using R Classification and Regression 李欣海 中科院动物所 邮件：lixh@ 主页：/~LiXinhai 博客：/u/lixinhai 微博：/lixinhaiblog 2/25 随机森林简介 第五届中国R语言会议 北京2012 李欣海 Random Forest /2011/03/ an-introduction-to-data-mining-for-marketing-and-business-intelligence/ • Random Forest is an ensemble classifier that consists of many decision trees. • It outputs the class that is the mode of the classs output by individual trees (Breiman 2001). • It deals with “small n large p”-problems, high-order interactions, correlated predictor variables. Breiman, L. 2001. Random forests. Machine Learning 45:5-32. (Being cited 6500 times until 2012) 3/25 随机森林简介 第五届中国R语言会议 北京2012 李欣海 History /2011/03/ an-introduction-to-data-mining-for-marketing-and-business-intelligence/ The algorithm for inducing a random forest was developed by Leo Breiman (2001) and Adele Cutler, and Random Forests is their trademark. The term came from random decision forests that was first proposed by Tin Kam Ho of Bell Labs in 1995. The method combines Breimans bagging idea and the random selection of features, introduced independently by Ho (1