  Random forests are decision tree ensembles that can be used to solve a
variety of machine learning problems. However, as the number of trees and their
individual size can be large, their decision making process is often
incomprehensible. In order to reason about the decision process, we propose
representing it as an argumentation problem. We generalize sufficient and
necessary argumentative explanations using a Markov network encoding, discuss
the relevance of these explanations and establish relationships to families of
abductive explanations from the literature. As the complexity of the
explanation problems is high, we discuss a probabilistic approximation
algorithm and present first experimental results.
