Hello,
I have a question regarding the ROC/AUC calculation in GeNIe for binary classification.
For a binary node, GeNIe reports a separate ROC curve and AUC for each state. For example:
Example using the standard ALARM network and 1000 simulated cases, node "Disconnect" (states True/False), tested with several other nodes as simultaneous class nodes (test-only validation, no evidence removed except the class nodes):
- ROC curve for Disconnect=True: AUC = 0.919541
- ROC curve for Disconnect=False: AUC = 0.886006
Other example. Just testing the one-node class:
FiO2 = Normal: AUC = 0.675179
FiO2 = Low: AUC = 0.672842
Since there are only two states, I would have expected the AUC values to be identical because the posterior probability of one state is the complement of the other.
Is it expected that the reported AUC values differ slightly between the two states? If so, could you explain what causes this difference?
Thank you!