For some use cases, it may be important to treat a prediction for which no corresponding actual label has been logged yet as having a default negative class actual label. For example, consider tracking advertisement conversion rates for an ad clickthrough rate model, where the positive class is
click and the negative class is
no_click. For ad conversion purposes, a prediction without a corresponding actual label for an ad placement is equivalent to logging an explicit
no_click actual label for the prediction. In both cases, the result is the same: a user has not converted by clicking on the ad.
For AUC-ROC, PR-AUC, and Log Loss performance metrics, Arize supports treating predictions without an explicit actual label as having the negative class actual label by default. In the above example, a
click prediction without an actual would be treated as a false positive, because the missing actual for the prediction would by default be assigned to the
no_click negative class.