问题
I have a dataset looks like this:
data.flu <- data.frame(chills = c(1,1,1,0,0,0,0,1), runnyNose = c(0,1,0,1,0,1,1,1), headache = c("M", "N", "S", "M", "N", "S", "S", "M"), fever = c(1,0,1,1,0,1,0,1), flu = c(0,1,1,1,0,1,0,1) )
> data.flu
chills runnyNose headache fever flu
1 1 0 M 1 0
2 1 1 N 0 1
3 1 0 S 1 1
4 0 1 M 1 1
5 0 0 N 0 0
6 0 1 S 1 1
7 0 1 S 0 0
8 1 1 M 1 1
> str(data.flu)
'data.frame': 8 obs. of 5 variables:
$ chills : num 1 1 1 0 0 0 0 1
$ runnyNose: num 0 1 0 1 0 1 1 1
$ headache : Factor w/ 3 levels "M","N","S": 1 2 3 1 2 3 3 1
$ fever : num 1 0 1 1 0 1 0 1
$ flu : num 0 1 1 1 0 1 0 1
Why predict function returns me nothing?
# I can see the model has been successfully created.
model <- naiveBayes(flu~., data=data.flu)
# I created a new data
patient <- data.frame(chills = c(1), runnyNose = c(0), headache = c("M"), fever = c(1))
> predict(model, patient)
factor(0)
Levels:
# I tried with the training data, still won't work
> predict(model, data.flu[,-5])
factor(0)
Levels:
I tried following the examples in the help manual in naiveBayes and it works for me. I am not sure what is wrong with my approach. Thanks a lot!
I think there might be something wrong with the data type before applying the naivebayes model, I tried to change all the variables to factor using as.factor and it seems like working for me. But I am still super confused what is the 'How' and 'Why' behind the scene.
回答1:
Problem isn't in the predict() function but in your model definition.
Help file of naiveBayes() says:
Computes the conditional a-posterior probabilities of a categorical class variable
given independent predictor variables using the Bayes rule.
So y values should be categorical but in your case they are numeric.
Solution is to convert flu to factor.
model <- naiveBayes(as.factor(flu)~., data=data.flu)
predict(model, patient)
[1] 1
Levels: 0 1
来源：https://stackoverflow.com/questions/19961441/naivebayes-in-r-cannot-predict-factor0-levels