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

Understand

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.