cmcdh集群管理课程bayes分类

1、问题引样本：10000 封邮件，每个邮件被标记记已有公问题分判为 还是与及假设文档单词出现独是指的:问题引样本：10000 封邮件，每个邮件被标记记已有公问题分判为 还是与及假设文档单词出现独是指的:c1类别的出现的单词总数Px|c)*P(cPx|)*P(x|c).)P(c1N12P(x|c)*P(c实例说0101011 my,dog,has,flea,problems,实例说0101011 my,dog,has,flea,problems,help,maybe,not,take,him,to,dog,park,my,dalmation,is,so,cute,I,love,stop,ting,

2、worthless,mr,licks,ate,my,steak,how,to,stop,quit,buying,worthless,dog,food,性，0 代表文本形形式利于判断。通常将文本转换为词向量向量空间模型) my, dog, has, flea, problems, help, please, maybe, not, take, him, to, park, ,dalmation, is, so, cute, I, love, stop, how, quit, buying, foodting, worthless, garbage, mr, licks, ate, my,dog,

3、has,flea,problems,help,1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 词集模型只标识了单词在一篇文档中是否出现代码实代码实先验概3性，类条件概 及性中，单词dog出现的概率000my,dog,has,flea,先验概3性，类条件概 及性中，单词dog出现的概率000my,dog,has,flea,problems,help,my,dalmation,is,so,cute,I,love,mr,licks,ate,my,steak,how

4、,to,stop,9中单词dog出现的次数111stop, ting, worthless, 56quit,buying,worthless,dog,food,dogg| 辱常) 性1侮常辱性1词词总总2数数1maybe,not,take,him,to,dog,park,8性所有单词总数：5+6+8=19 dog2 性与存在问01maybe,not,take,him,to,dog,park,8性所有单词总数：5+6+8=19 dog2 性与存在问0 x1 没有出现过。P(x|c)*P(x|c).P(x ( |)lo(x(x|P c 11 11 21xNN1|c2111)211logP(x2 代

5、码实 代码实n1p(x|c11 n2类条件概率及先验概类条件概率及先验概-2.56494936 -2.56494936 -2.56494936 -3.25809654 -3.25809654 -2.56494936 -2.56494936 -3.25809654 -2.56494936 -2.56494936 -2.56494936 -3.25809654 -3.25809654 -2.15948425 -3.25809654 -2.56494936 -3.25809654 -2.56494936 -2.56494936 -3.25809654 -2.56494936 -2.56494936 -3.25809654 -2.56494936 -3.25809654 -3.04452244 -3.04452244 -3.04452244 -2.35137526 -2.35137526 -3.04452244 -3.04452244 -2.35137526 -2.35137526 -3.04452244 -3.04452244 -2.35137526 -2.35137526 -2.35137526 -2.35137526 -3.04452244 -1.94591015 -3.0445