| Kurzfassung
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PRIM (Patient Rule Induction Method) is an algorithm for discovering scenarios from simulations, by creating hyperboxes, that are human-comprehensible. Yet PRIM alone requires relatively large datasets and computational simulations are usually quite expensive. Consequently, one wants to obtain a plausible scenario, with a minimal number of simulations. It has been shown, that combining PRIM with ML models, which generalize faster, can reduce the number of necessary simulation runs by around 75%.
In this thesis, I analyze different Active Learning (AL) sampling strategies together with several intermediate ML models, in order to find out if AL can systematically improve existing scenario discovery methods and if a most beneficial combination of sampling method and intermediate ML model exists for this purpose.
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