SMACE: A New Method for the Interpretability of Composite Decision Systems
Abstract
Interpretability is a pressing issue for decision systems. Many post hoc methods have been proposed to explain the predictions of a single machine learning model. However, business processes and decision systems are rarely centered around a unique model. These systems combine multiple models that produce key predictions, and then apply decision rules to generate the final decision. To explain such decisions, we propose the Semi-Model-Agnostic Contextual Explainer (SMACE), a new interpretability method that combines a geometric approach for decision rules with existing interpretability methods for machine learning models to generate an intuitive feature ranking tailored to the end user. We show that established model-agnostic approaches produce poor results on tabular data in this setting, in particular giving the same importance to several features, whereas SMACE can rank them in a meaningful way.
Cite
@article{arxiv.2111.08749,
title = {SMACE: A New Method for the Interpretability of Composite Decision Systems},
author = {Gianluigi Lopardo and Damien Garreau and Frederic Precioso and Greger Ottosson},
journal= {arXiv preprint arXiv:2111.08749},
year = {2023}
}
Comments
Accepted to ECML PKDD 2022, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases