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Supervised learning models are one of the most fundamental classes of models.Viewing supervised learning from a probabilistic perspective, the set oftraining data to which the model is fitted is usually assumed to follow astationary distribution. However, this stationarity assumption is oftenviolated in a phenomenon called concept drift, which refers to changes overtime in the predictive relationship between covariates $\mathbf{X}$ and aresponse variable $Y$ and can render trained models suboptimal or obsolete. Wedevelop a comprehensive and computationally efficient framework for detecting,monitoring, and diagnosing concept drift. Specifically, we monitor the Fisherscore vector, defined as the gradient of the log-likelihood for the fittedmodel, using a form of multivariate exponentially weighted moving average,which monitors for general changes in the mean of a random vector. In spite ofthe substantial performance advantages that we demonstrate over popularerror-based methods, a score-based approach has not been previously consideredfor concept drift monitoring. Advantages of the proposed score-based frameworkinclude applicability to any parametric model, more powerful detection ofchanges as shown in theory and experiments, and inherent diagnosticcapabilities for helping to identify the nature of the changes.

Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors