R package caret confusionMatrix with missing categories


I am using the function confusionMatrix in the R package caret to calculate some statistics for some data I have. I have been putting my predictions as well as my actual values into the table function to get the table to be used in the confusionMatrix function as so:

table(predicted,actual)

However, there are multiple possible outcomes (e.g. A, B, C, D), and my predictions do not always represent all the possibilities (e.g. only A, B, D). The resulting output of the table function does not include the missing outcome and looks like this:

    A    B    C    D
A  n1   n2   n2   n4  
B  n5   n6   n7   n8  
D  n9  n10  n11  n12
# Note how there is no corresponding row for `C`.

The confusionMatrix function can't handle the missing outcome and gives the error:

Error in !all.equal(nrow(data), ncol(data)) : invalid argument type

Is there a way I can use the table function differently to get the missing rows with zeros or use the confusionMatrix function differently so it will view missing outcomes as zero?

As a note: Since I am randomly selecting my data to test with, there are times that a category is also not represented in the actual result as opposed to just the predicted. I don't believe this will change the solution.


Answers:


You can use union to ensure similar levels:

library(caret)

# Sample Data
predicted <- c(1,2,1,2,1,2,1,2,3,4,3,4,6,5) # Levels 1,2,3,4,5,6
reference <- c(1,2,1,2,1,2,1,2,1,2,1,3,3,4) # Levels 1,2,3,4

u <- union(predicted, reference)
t <- table(factor(predicted, u), factor(reference, u))
confusionMatrix(t)