  Finding regions for which there is higher controversy among different
classifiers is insightful with regards to the domain and our models. Such
evaluation can falsify assumptions, assert some, or also, bring to the
attention unknown phenomena. The present work describes an algorithm, which is
based on the Exceptional Model Mining framework, and enables that kind of
investigations. We explore several public datasets and show the usefulness of
this approach in classification tasks. We show in this paper a few interesting
observations about those well explored datasets, some of which are general
knowledge, and other that as far as we know, were not reported before.
