数据分析与挖掘 - R语言：贝叶斯分类算法（案例三）

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。