# Bagging

​ Bagging算法是另外一类，是一大家族，可以**并行计算**，不像前面的AdaBoost，是一个依赖一个的串行的。这种集成算法数学性并不要求很高。简单易理解。

## 基本流程

> 1）基于自助采样给定m个数据，有放回的采样m次，得到一个包含m个数据的数据集（有些会出现多次，有些不出现，但理论上只有63.2%的概率出现在集合中）。
>
> 2）自助采样T次，就得到了T个含m个数据的数据集。
>
> 3）T次采样每次采样完都训练出一个弱学习器，这样得到T个弱学习器了。
>
> 4）预测，对分类任务：使用投票多数表决了；回归：简单平均。

典型代表：随机森林等，后面具体讲解


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