When I was learning data-analysis course online, the lecturer spoke of two advantages of ROC curve. He said "that AUC results do not change with changes in the incidents of the actual condition, nor is AUC affected by changes in the relative cost of the two different types of binary classification errors, false positives and false negatives. Therefore, when either future incidents or the cost of classification errors or both are unstable or cannot be known, the AUC is generally the best possible performance metric available."
One thing I was confused about was why ROC curve will not be affected by the changes in the incidents of the actual condition. Based on what I know, ROC is mapped out from the data people have been collected, or the curve's x and y location is the true negative rate and the true positive rate at different thresholds, which are all related to events.
Therefore, could anyone explain what the lecturer's meaning? Thanks a lot.