# ROC and accuracy results: how can AUC be one if the classifier has made mistakes?

Trying to get the accuracy and the ROC curve with R (mlr package) I get the following results:

Absolute confusion matrix:
predicted
true     CHAV PMP -err.-
CHAV      4   0      0
PMP       2   5      2
-err.-    2   0      2

predicted
true   CHAV      PMP
CHAV 1         0      tpr: 1    fnr: 0
PMP  0.29      0.71   fpr: 0.29 tnr: 0.71
ppv: 0.67 for: 0 lrp: 3.5  acc: 0.82
fdr: 0.33 npv: 1 lrm: 0    dor: Inf


It is clear that the model has two mistakes (82% accurracy)

But when I try to calculate the AUC, I get the following result:

auc
1


I don't understand why the AUC is 1, even though the classifier has made 2 mistakes.