Gossen, FrederikSteffen, Bernhard2022-03-102022-03-102021-09-29http://hdl.handle.net/2003/4077810.17877/DE290R-22635Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise the outcome of their predictions. However, this comes at a cost: it is increasingly difficult to understand why a Random Forest made a specific choice, and its running time for classification grows linearly with the size (number of trees). In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram which has the following two effects: (1) minimal, sufficient explanations for Random Forest-based classifications can be obtained by means of a simple three step reduction, and (2) the running time is radically improved. In fact, our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure.enInternational journal on software tools for technology transfer;Vol. 23. 2021, Art. Nr. 635http://creativecommons.org/licenses/by/4.0/Random forestAlgebraic decision diagramAggregationExplainabilityInterpretabilityRunning time optimisationMemory optimisation004article (journal)EntscheidungsgraphAggregationLaufzeitErklärungKlassifikator <Informatik>Speicher <Informatik>Optimierung