浅谈利用逻辑回归来解决文本分类时的模型调优

想和数据挖掘沾点边，所以最近在复习一些算法，因为又学了点R，深感这是个统计分析挖掘的利器，所以想用R实现一些挖掘算法。

朴素贝叶斯法大概是最简单的一种挖掘算法了，《统计学习方法》在第四章做了很详细的叙述，无非是对于输入特征x，利用通过学习得到的模型计算后验概率分布，将后验概率最大的分类作为输出。

根据贝叶斯定理，后验概率P(Y=cx | X=x) = 条件概率P(X=x | Y=cx) * 先验概率P(Y = ck) / P(X=x)，取P(X=x | Y=cx) * P(Y = ck)最大的分类作为输出。
下面是一个小数据集下使用R进行朴素贝叶斯分类的例子,代码如下：
    #构造训练集  
    data <- matrix(c("sunny","hot","high","weak","no",  
                     "sunny","hot","high","strong","no",  
                     "overcast","hot","high","weak","yes",  
                     "rain","mild","high","weak","yes",  
                     "rain","cool","normal","weak","yes",  
                     "rain","cool","normal","strong","no",  
                     "overcast","cool","normal","strong","yes",  
                     "sunny","mild","high","weak","no",  
                     "sunny","cool","normal","weak","yes",  
                     "rain","mild","normal","weak","yes",  
                     "sunny","mild","normal","strong","yes",  
                     "overcast","mild","high","strong","yes",  
                     "overcast","hot","normal","weak","yes",  
                     "rain","mild","high","strong","no"), byrow = TRUE,  
                   dimnames = list(day = c(),  
                   condition = c("outlook","temperature",  
                     "humidity","wind","playtennis")), nrow=14, ncol=5);  
      
    #计算先验概率  
    prior.yes = sum(data[,5] == "yes") / length(data[,5]);  
    prior.no  = sum(data[,5] == "no")  / length(data[,5]);  
      
    #模型  
    naive.bayes.prediction <- function(condition.vec) {  
        # Calculate unnormlized posterior probability for playtennis = yes.  
        playtennis.yes <-  
            sum((data[,1] == condition.vec[1]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(outlook = f_1 | playtennis = yes)  
            sum((data[,2] == condition.vec[2]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(temperature = f_2 | playtennis = yes)  
            sum((data[,3] == condition.vec[3]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(humidity = f_3 | playtennis = yes)  
            sum((data[,4] == condition.vec[4]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(wind = f_4 | playtennis = yes)  
            prior.yes; # P(playtennis = yes)  
      
        # Calculate unnormlized posterior probability for playtennis = no.  
        playtennis.no <-  
            sum((data[,1] == condition.vec[1]) & (data[,5] == "no"))  / sum(data[,5] == "no")  * # P(outlook = f_1 | playtennis = no)  
            sum((data[,2] == condition.vec[2]) & (data[,5] == "no"))  / sum(data[,5] == "no")  * # P(temperature = f_2 | playtennis = no)  
            sum((data[,3] == condition.vec[3]) & (data[,5] == "no"))  / sum(data[,5] == "no")  * # P(humidity = f_3 | playtennis = no)  
            sum((data[,4] == condition.vec[4]) & (data[,5] == "no"))  / sum(data[,5] == "no")  * # P(wind = f_4 | playtennis = no)  
            prior.no; # P(playtennis = no)  
          
        return(list(post.pr.yes = playtennis.yes,  
                post.pr.no  = playtennis.no,  
                prediction  = ifelse(playtennis.yes >= playtennis.no, "yes", "no")));  
    }  
      
    #预测  
    naive.bayes.prediction(c("rain",     "hot",  "high",   "strong"));  
    naive.bayes.prediction(c("sunny",    "mild", "normal", "weak"));  
    naive.bayes.prediction(c("overcast", "mild", "normal", "weak")); 
最后一个分类预测结果如下：
$post.pr.yes
[1] 0.05643739

$post.pr.no
[1] 0

$prediction
[1] "yes"