Generalized classification accuracy metric that coincides with accuracy metric when classes are balanced Suppose you create a classifier for a dataset where there is a big disparity between the two classes, e.g., in fraud detection most of your data will be non-fraudulent. It's known that the standard accuracy metric can be very misleading in such cases.
Question: Is there an accuracy metric that is adjusted to the imbalance between classes and that exactly coincides with the standard accuracy metric when the classes are perfectly balanced?

Yes, there is such a metric and it's called balanced accuracy. It's the arithmetic mean of true positive and negative rates (TPR and TNR(. Here is some intuition on it. The balancing logic explained in the above post can be easily extended to multi class case. Or, you can still use TPR and TNR of each class to determine it (an example here).
