中文说明：bagging 集成算法描述：Bagging是一种把多个不同的弱学习器训练成一个强学习器的集成学习方法Bagging是一种并行训练过程,通过分类测试样本的有放回抽样,获取多个分类测试子样本,通过分类子样本训练T个基分类器，当对每一个实例进行分类时，分别调用这T个基分类器，得到T个结果，最后对分类问题把这T个分类结果中出现次数最多的类赋予测试实例。
English Description：
Bagging ensemble algorithm description: bagging is an ensemble learning method that trains multiple different weak learners into a strong learner. Bagging is a parallel training process. Multiple subsamples of classification test are obtained by putting back samples of classification test samples, T base classifiers are trained by classification sub samples. When each instance is classified, the T base classifiers are called respectively to get t results. Finally, the test instance is given to the class with the most frequent occurrence in the T classification results.