From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection

ArXi:2412.11308v2 Announce Type: replace-cross Predictive models often degrade in performance due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and adapt to. Traditional drift detection methods often rely on metrics such as accuracy or marginal variable distributions, which may fail to capture subtle but important conceptual changes.