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案例三比较简单，不需要自己写公式算法，使用了R自带的naiveBayes函数。
代码如下：
> library(e1071)
> classifier<-naiveBayes(iris[,1:4], iris[,5])
#或写成下面形式，都可以。
> classifier<- naiveBayes(Species ~ ., data = iris) #其中Species是类别变量
#预测
> predict(classifier, iris[1, -5])
预测结果为：
[1] setosa
Levels: setosa versicolor virginica
和原数据一样！
*********************************这里是分割线**************************************
我们再拿这个方法来预测一下案例一中的样本。
#样本数据集： mydata <- 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, nrow=14, ncol=5) #添加列名： colnames(mydata) <- c("outlook","temperature","humidity","wind","playtennis") #贝叶斯算法： m<-naiveBayes(mydata[,1:4], mydata[,5]) #或使用下面的方法 m<- naiveBayes(playtennis ~ ., data = mydata)
#报错：Error in sum(x) : invalid ‘type‘ (character) of argument 无效的类型，只能是数字？ #创建预测数据集： new_data = data.frame(outlook="rain", temperature="cool", humidity="normal", wind="strong", playtennis="so") #预测： predict(m, new_data)
在使用naiveBayes函数时报错：Error in sum(x) : invalid ‘type‘ (character) of argument
我们看一下官方文档，对data有这样一句描述：
data Either a data frame of predictors (categorical and/or numeric) or a contingency table.
data是一个数字类型的数据框。
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原文地址：http://www.cnblogs.com/hunttown/p/5526786.html
