使用朴素贝叶斯预测新值

Question

我有一个看起来像这样的数据框

weather <- c("good", "good", "good", "bad", "bad", "good")
temp <- c("high", "low", "low", "high", "low", "low")
golf <- c("yes", "no", "yes", "no", "yes" , "no")
df <- data.frame(weather, temp, golf)

我现在想做的是使用朴素贝叶斯方法得到这个新数据集的概率

df_new <- data.frame(weather = "good", temp = "low")

所以我试试

library(e1071)
model <- naiveBayes(golf ~.,data=df)
predict(model, df_new)

但这给了我：

NO

知道如何将其转化为概率吗？

Answer 1

如果您使用 type = "raw"

，则返回概率

predict(model, df_new, type = "raw")
no yes
[1,] 0.5 0.5

predict(model, df_new, type = "class")
[1] no
Levels: no yes

使用朴素贝叶斯预测新值

Using naive bayes to predict new values

r

naivebayes