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Finding regions for which there is higher controversy among differentclassifiers is insightful with regards to the domain and our models. Suchevaluation can falsify assumptions, assert some, or also, bring to theattention unknown phenomena. The present work describes an algorithm, which isbased on the Exceptional Model Mining framework, and enables that kind ofinvestigations. We explore several public datasets and show the usefulness ofthis approach in classification tasks. We show in this paper a few interestingobservations about those well explored datasets, some of which are generalknowledge, and other that as far as we know, were not reported before.

Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally