Découverte de sous-groupes de prédictions interprétables pour le triage d’incidents
In Extraction et gestion des connaissances, EGC 2022, blois, france, 24 au 28 janvier 2022
Abstract
The need for predictive maintenance comes with an increasing number of incidents, where it is imperative to quickly decide which service to contact for corrective actions. Several predictive models have been designed to automate this process, but the efficient models are opaque (say, black boxes). Many approaches have been proposed to locally explain each prediction of such models. However, providing an explanation for every result is not conceivable when it comes to a large number of daily predictions to analyze. In this article we propose a method based on Subgroup Discovery in order to (1) group together objects that share similar explanations and (2) provide a description that characterises each subgroup