Formal methods are routinely used to address the issue of
explainability of machine learning models. Yet, it is not always trivial
to understand how a logical explanation could be useful in practice due
to human readability challenges. This paper applies classical geometric
methods for interpreting logical explanations and illustrates the usefulness
for the users on datasets from medical and image classification domains
previously studied in the context of formal explainability.