Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence
Eliana Pastor, Luca de Alfaro, Elena Baralis · 2021
Machine learning models may perform differently on different data subgroups, which we represent as itemsets (i.e., conjunctions of simple predicates). The identification of these critical data subgroups plays an important role in many applications, for example model validation and testing, or evaluation of model fairness. Typically, domain expert help is required to identify relevant (or sensitive) subgroups.