R package caret confusionMatrix with missing categories

Question

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.

Solution 1

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)

Solution 2

First note that confusionMatrix can be called as confusionMatrix(predicted, actual) in addition to being called with table objects. However, the function throws an error if predicted and actual (both regarded as factors) do not have the same number of levels.

This (and the fact that the caret package spit an error on me because they don't get the dependencies right in the first place) is why I'd suggest to create your own function:

# Create a confusion matrix from the given outcomes, whose rows correspond
# to the actual and the columns to the predicated classes.
createConfusionMatrix <- function(act, pred) {
  # You've mentioned that neither actual nor predicted may give a complete
  # picture of the available classes, hence:
  numClasses <- max(act, pred)
  # Sort predicted and actual as it simplifies what's next. You can make this
  # faster by storing `order(act)` in a temporary variable.
  pred <- pred[order(act)]
  act  <- act[order(act)]
  sapply(split(pred, act), tabulate, nbins=numClasses)
}

# Generate random data since you've not provided an actual example.
actual    <- sample(1:4, 1000, replace=TRUE)
predicted <- sample(c(1L,2L,4L), 1000, replace=TRUE)

print( createConfusionMatrix(actual, predicted) )

which will give you:

      1  2  3  4
[1,] 85 87 90 77
[2,] 78 78 79 95
[3,]  0  0  0  0
[4,] 89 77 82 83

Comments

[topepo]

This is correct. You should have the same levels in the observed and predicted so that the full table is shown. How else would table know that other factor levels are possible? Most of the functions in caret go to great lengths to ensure predictions always have the same levels as the original classes - Max

[fotNelton]

table could tell from the union of the actual and predicted levels. I can however accept (what else could I do anyway :-)) that table works this way, I just thought I should mention the fact that for this particular problem it won't work as the OP wishes.

[Barker]

Thank you so much, this was a huge help!. I added some code to create the matrix if 'act' does not have all the possible values represented as well as to be able to label the rows and columns and it worked perfectly. Also as a note to others, you need to use 'as.table' function to make it work in the 'confusionMatrix' function.

[Joonhwan]

For me, because my factor levels are not continuous, fotNelton's method was not applicable. But this works, thanks.

[Mohammad Mazraeh]

Good and Simple! I would Edit this two lines in the function: numClasses <- length(unique(c(act,pred))) sapply(split(as.factor(pred), as.factor(act)), tabulate, nbins=numClasses)