Seven novel interestingness measures are presented that allow to evaluate different aspects of probabilistic generative classifiers.

Three case studies utilizing 21 artificial and real-world benchmark data sets illustrate the usefulness of our measures in different application scenarios.

We show that our measures can help researchers in three ways: the training process of a classifier can be improved, the trained classifier can be evaluated and simplified if desired, and during the application phase the classifier can be automatically supervised using interestingness evaluations.