how to pass a tibble to caret::confusionmatrix()?

Consider this simple example:

data_frame(truth = c(1,1,0,0),
           prediction = c(1,0,1,0),
           n_obs = c(100,10,90,50))
# A tibble: 4 x 3
  truth prediction n_obs
  <dbl>      <dbl> <dbl>
1     1          1   100
2     1          0    10
3     0          1    90
4     0          0    50

I would like to pass this tibble to caret::confusionMatrix so that I have all the metrics I need at once (accuracy, recall, etc).

As you can see, the tibble contains all the information required to compute performance statistics. For instance, you can see that in the test dataset (not available here), there are 100 observations where the predicted label 1 matched the true label 1. However, 90 observations with a predicted value of 1 were actually false positives.

I do not want to compute all the metrics by hand, and would like to resort to caret::confusionMatrix()

However, this has proven to be suprisingly difficult. Calling confusionMatrix(.) on the tibble above does not work. Is there any solution here?

Thanks!

You could use the following. You have to set the positive class to 1 otherwise 0 will be taken as the positive class.

confusionMatrix(xtabs(n_obs ~ prediction + truth , df), positive = "1")

Confusion Matrix and Statistics

          truth
prediction   0   1
         0  50  10
         1  90 100

               Accuracy : 0.6             
                 95% CI : (0.5364, 0.6612)
    No Information Rate : 0.56            
    P-Value [Acc > NIR] : 0.1128          

                  Kappa : 0.247           
 Mcnemar's Test P-Value : 2.789e-15       

            Sensitivity : 0.9091          
            Specificity : 0.3571          
         Pos Pred Value : 0.5263          
         Neg Pred Value : 0.8333          
             Prevalence : 0.4400          
         Detection Rate : 0.4000          
   Detection Prevalence : 0.7600          
      Balanced Accuracy : 0.6331          

       'Positive' Class : 1