Explaining Random Forests Using Bipolar Argumentation and Markov Networks
Nico Potyka, Xiang Yin, Francesca Toni
Abstract
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. We show that their decision process can be naturally represented as an argumentation problem, which allows creating global explanations via argumentative reasoning. 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 present an efficient approximation algorithm with probabilistic approximation guarantees.
BibTeX
@article{Potyka_Yin_Toni_2023, title={Explaining Random Forests Using Bipolar Argumentation and Markov Networks}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26132}, DOI={10.1609/aaai.v37i8.26132}, abstractNote={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.
We show that their decision process can be naturally represented as an argumentation problem, which allows creating global explanations via argumentative reasoning. 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 present an efficient approximation algorithm with probabilistic approximation guarantees.}, number={8}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Potyka, Nico and Yin, Xiang and Toni, Francesca}, year={2023}, month={Jun.}, pages={9453-9460} }