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Random forests are decision tree ensembles that can be used to solve avariety of machine learning problems. However, as the number of trees and theirindividual size can be large, their decision making process is oftenincomprehensible. In order to reason about the decision process, we proposerepresenting it as an argumentation problem. We generalize sufficient andnecessary argumentative explanations using a Markov network encoding, discussthe relevance of these explanations and establish relationships to families ofabductive explanations from the literature. As the complexity of theexplanation problems is high, we discuss a probabilistic approximationalgorithm and present first experimental results.

Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)