  This article proposes a test procedure that can be used to test ML models and
ML-based systems independently of the actual training process. In this way, the
typical quality statements such as accuracy and precision of these models and
system can be verified independently, taking into account their black box
character and the immanent stochastic properties of ML models and their
training data. The article presents first results from a set of test
experiments and suggest extensions to existing test methods reflecting the
stochastic nature of ML models and ML-based systems.
