Question

I am trying to do naive bayes classification in R. I have seen this example in following link.

http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/Na%C3%AFve_Bayes

Only 2 lines are there. First classify and then predict.

> classifier<-naiveBayes(iris[,1:4], iris[,5]) 
> table(predict(classifier, iris[,-5]), iris[,5])

This same code on “iris dataset” working fine. But when i applied the same on my dataset, I am getting some errors.

My dataset contains 4 attributes and 4th attribute the class attribute.

> str(data1)
'data.frame':   1370 obs. of  4 variables:
 $ TenScore     : num  85 84.2 67.2 91.5 79.3 ...
 $ TwelthScore  : num  69 87.9 67.5 82.7 72.4 ...
 $ GDegreeScore : num  63.3 70.7 61.3 78.2 62.1 ...
 $ Got_Admission: chr  "No" "No" "No" "No" ...

So, I tried this.

> classifier<-naiveBayes(data1[,1:3], data1[,4]) 
> table(predict(classifier, data1[,-4]), data1[,4])
Error in table(predict(classifier, data1[, -4]), data1[, 4]) : 
  all arguments must have the same length

I am getting above error when I am executing the command. When I just use predict, its giving me following output.

> predict(classifier, data1[,-4])
factor(0)
Levels:

str(data1) 'data.frame': 1370 obs. of 4 variables: 
$ TenScore : num 85 84.2 67.2 91.5 79.3 ... 
$ TwelthScore : num 69 87.9 67.5 82.7 72.4 ... 
$ GDegreeScore : num 63.3 70.7 61.3 78.2 62.1 ... 
$ Got_Admission: chr "No" "No" "No" "No" ...

Please explain me whats the errors about and how to solve?

Answer 1

I can produce the same error by changing the 5th column of iris to character:

> iris[ , 5] <- as.character(iris[ , 5] )
> classifier<-naiveBayes(iris[,1:4], iris[,5]) 
>  table(predict(classifier, iris[,-5]), iris[,5])
Error in table(predict(classifier, iris[, -5]), iris[, 5]) : 
  all arguments must have the same length
#  The fix -------->
 iris[ , 5] <- factor(as.character(iris[ , 5] ))
 classifier<-naiveBayes(iris[,1:4], iris[,5]) 
 table(predict(classifier, iris[,-5]), iris[,5])
#  ---- output--------            
             setosa versicolor virginica
  setosa         50          0         0
  versicolor      0         47         3
  virginica       0          3        47

So you should probably do this:

  data1$ Got_Admission <- factor(data1$ Got_Admission)

If your ‘Got_Admission’ column is not in good order you will get confusing results (the GIGO effect). You should first look at the contents with:

table(data1$ Got_Admission)