# ROC curve and confusion matrix in classifier performance evaluation

I applied two different classifiers against the same validation set. It turns out that classifier A is better than classifier B in terms of ROC curve. However, classifier B is better than classifier A in terms of confusion matrix. How to explain this kind of contradiction?

• Add data and graphs. Otherwise it's difficult to know what you mean. Also, what does "better in terms of confusion matrix" mean? You can order them by accuracy, or f-score, or precision, or recall, or sensitivity, or specificity, .... – Stumpy Joe Pete Mar 16 '13 at 22:26
• You still haven't told us what it means to be "better" in terms of confusion matrix. There isn't only one way to compare confusion matrices! – Stumpy Joe Pete Mar 17 '13 at 4:14

A ROC curve shows you performance across a range of different classification thresholds and a confusion matrix only shows you one (typically when $Pr(y > .5)$).