數據分析與挖掘 - R語言：貝葉斯分類算法（案例三）

案例三比較簡單，不需要自己寫公式算法，使用了R自帶的naiveBayes函數。

 

代碼如下：

> library(e1071)
> classifier<-naiveBayes(iris[,1:4], iris[,5]) 
#或寫成下面形式，都可以。
> classifier<- naiveBayes(Species ~ ., data = iris)    #其中Species是類別變量

#預測
> predict(classifier, iris[1, -5])

預測結果為：

[1] setosa
Levels: setosa versicolor virginica

和原數據一樣！

 

*********************************這里是分割線**************************************

我們再拿這個方法來預測一下案例一中的樣本。

#樣本數據集：
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")

#貝葉斯算法：
m<-naiveBayes(mydata[,1:4], mydata[,5]) 
#或使用下面的方法
m<- naiveBayes(playtennis ~ ., data = mydata)    
#報錯：Error in sum(x) : invalid 'type' (character) of argument 無效的類型，只能是數字？

#創建預測數據集：
new_data = data.frame(outlook="rain", temperature="cool", humidity="normal", wind="strong", playtennis="so")

#預測：
predict(m, new_data)

在使用naiveBayes函數時報錯：Error in sum(x) : invalid 'type' (character) of argument

我們看一下官方文檔，對data有這樣一句描述：

data　　Either a data frame of predictors (categorical and/or numeric) or a contingency table.

data是一個數字類型的數據框。