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。