random forest
a set of decision trees, make classification by voting (maybe with some weight)
多颗决策树, 采用类似投票的方式（可以占一定比重）决定分类
bagging and boosting
letting weak models consist of strong model
用多个弱模型组成强模型
bagging
randomly sampling the training data with replacement
generally 68% non-repeating is selected from the data
repeat the operation above in each classifier (like SVM, decision tree...)
Advantage: reduce the variance
有放回地随机选择与样本集等大的样本
不重复的样本约 68%
对每个分类器重复上述操作, 比如支持向量机, 决策树
优点: 降低方差
boosting (here directly introduce adaptive boosting)
do default training in the first classifier
raise the weight of mis-classified samples in the training of the next classifier
repeat the operation above
assign weight to each classifier based on the accuracy
对第一个分类器做默认训练
提高被错分的样本在下一个分类器的训练时的权重
重复上述操作
基于准确率对每个分类器分配权重
标签：阐述,weight,ML,样本,training,分类器,概念,boosting,classifier From： https://www.cnblogs.com/LacLic/p/17876307.html