集成学习ensemble

集成学习里面有两大派：Bagging和Boosting，每一派都有其代表性算法，这里给出一个大纲。

先来说下Bagging和Boosting之间的区别：bagging methods work best with strong and complex models (e.g., fully developed decision trees), in contrast with boosting methods which usually work best with weak models (e.g., shallow decision trees).

在说下不同Bagging方法之间的区别：有些子样本是子集，有些子样本是特征。

Bagging-Classifier和Regressor

　　Bagging

　　RandomForest

Boosting-Classifier和Regressor

　　AdaBoost

　　GradientBoosting

集成学习ensemble

原文：http://www.cnblogs.com/sylz/p/6858205.html