Building large IoT applications using a model-driven approach, has become an important methodology for building such applications. A tool intended to help with this process is the IoT Application Modelling tool developed at the University of Stuttgart. However, this tool is lacking validation capabilities. To implement validation in a (semi) automated manner, this work proposes an approach to assist the modeller with the task of selecting the best suited outlier detection method for a context model. based on existing or accumulated data. To achieve this a semi-automated wizard, integrated into the IoT Application Modelling Tool is proposed. Furthermore, concepts on how the resulting outlier detection can be deployed on the IoT infrastructure are discussed. The provided value and the usability of the tool are evaluated using a survey on a small set of researchers and IT professionals. The result of this survey have shown, that the proposed approach does partially automate and simplify the process of choosing an machine-learning based outlier detection method.