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The dynamicity of real-world systems poses a significant challenge todeployed predictive machine learning (ML) models. Changes in the system onwhich the ML model has been trained may lead to performance degradation duringthe system's life cycle. Recent advances that study non-stationary environmentshave mainly focused on identifying and addressing such changes caused by aphenomenon called concept drift. Different terms have been used in theliterature to refer to the same type of concept drift and the same term forvarious types. This lack of unified terminology is set out to create confusionon distinguishing between different concept drift variants. In this paper, westart by grouping concept drift types by their mathematical definitions andsurvey the different terms used in the literature to build a consolidatedtaxonomy of the field. We also review and classify performance-based conceptdrift detection methods proposed in the last decade. These methods utilize thepredictive model's performance degradation to signal substantial changes in thesystems. The classification is outlined in a hierarchical diagram to provide anorderly navigation between the methods. We present a comprehensive analysis ofthe main attributes and strategies for tracking and evaluating the model'sperformance in the predictive system. The paper concludes by discussing openresearch challenges and possible research directions.

From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors