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。

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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！

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*********************************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。

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