I want to compare a given classification algorithm with others via the Area under the (ROC-)curve metric. Unfortunately this algorithm only outputs the values of the respective confusion matrix (TP, FP, TN, FN) and a subset of the predicted positives, but no probability score for any of its predictions.
Confusion Matrix and Statistics Reference Prediction TRUE FALSE TRUE 11 10 FALSE 3 475 Accuracy : 0.9739 95% CI : (0.9559, 0.9861) No Information Rate : 0.9719 P-Value [Acc > NIR] : 0.46294 Kappa : 0.6156 Mcnemar's Test P-Value : 0.09609 Sensitivity : 0.78571 Specificity : 0.97938 Pos Pred Value : 0.52381 Neg Pred Value : 0.99372 Prevalence : 0.02806 Detection Rate : 0.02204 Detection Prevalence : 0.04208 Balanced Accuracy : 0.88255
When I tried to understand the ROC with examples like this or this, it always requires the prediction score to calculate the AUC and draw the curve. Wikipedia hints, that I should use a probability density function, but I don't know which and how. So, is it even possible to calculate the score and if yes, how?
Thank you guys in advance for your replies.