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rr-caretconfusion-matrix

How to send a confusion matrix to caret's confusionMatrix?

I'm looking at this data set: https://archive.ics.uci.edu/ml/datasets/Credit+Approval. I built a ctree:

myFormula<-class~.          # class is a factor of "+" or "-"
ct <- ctree(myFormula, data = train)

And now I'd like to put that data into caret's confusionMatrix method to get all the stats associated with the confusion matrix:

testPred <- predict(ct, newdata = test)

                #### This is where I'm doing something wrong ####
confusionMatrix(table(testPred, test$class),positive="+")
          ####  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ####

$positive
[1] "+"

$table
        td
testPred  -  +
       - 99  6
       + 20 88

$overall
      Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull AccuracyPValue  McnemarPValue 
  8.779343e-01   7.562715e-01   8.262795e-01   9.186911e-01   5.586854e-01   6.426168e-24   1.078745e-02 

$byClass
         Sensitivity          Specificity       Pos Pred Value       Neg Pred Value            Precision               Recall                   F1 
           0.9361702            0.8319328            0.8148148            0.9428571            0.8148148            0.9361702            0.8712871 
          Prevalence       Detection Rate Detection Prevalence    Balanced Accuracy 
           0.4413146            0.4131455            0.5070423            0.8840515 

$mode
[1] "sens_spec"

$dots
list()

attr(,"class")
[1] "confusionMatrix"

So Sensetivity is:

enter image description here (from caret's confusionMatrix doc)

If you take my confusion matrix:

$table
        td
testPred  -  +
       - 99  6
       + 20 88

You can see this doesn't add up: Sensetivity = 99/(99+20) = 99/119 = 0.831928. In my confusionMatrix results, that value is for Specificity. However Specificity is Specificity = D/(B+D) = 88/(88+6) = 88/94 = 0.9361702, the value for Sensitivity.

I've tried this confusionMatrix(td,testPred, positive="+") but got even weirder results. What am I doing wrong?

UPDATE: I also realized that my confusion matrix is different than what caret thought it was:

   Mine:               Caret:

            td             testPred
   testPred  -  +      td   -  +
          - 99  6        - 99 20
          + 20 88        +  6 88

As you can see, it thinks my False Positive and False Negative are backwards.

Solution

  • UPDATE: I found it's a lot better to send the data, rather than a table as a parameter. From the confusionMatrix docs:

    reference
    a factor of classes to be used as the true results

    I took this to mean what symbol constitutes a positive outcome. In my case, this would have been a +. However, 'reference' refers to the actual outcomes from the data set, aka the dependent variable.

    So I should have used confusionMatrix(testPred, test$class). If your data is out of order for some reason, it will shift it into the correct order (so the positive and negative outcomes/predictions align correctly in the confusion matrix.

    However, if you are worried about the outcome being the correct factor, install the plyr library, and use revalue to change the factor:

    install.packages("plyr")
library(plyr)
newDF <- df
newDF$class <- revalue(newDF$class,c("+"=1,"-"=0))
# You'd have to rerun your model using newDF

    I'm not sure why this worked, but I just removed the positive parameter:

    confusionMatrix(table(testPred, test$class))

    My Confusion Matrix:

            td
testPred  -  +
       - 99  6
       + 20 88

    Caret's Confusion Matrix:

            td
testPred  -  +
       - 99  6
       + 20 88

    Although now it says $positive: "-" so I'm not sure if that's good or bad.