SMACE: A New Method for the Interpretability of Composite Decision Systems

1 code implementation • 16 Nov 2021 • Gianluigi Lopardo, Damien Garreau, Frederic Precioso, Greger Ottosson

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.

BIG-bench Machine Learning