ADAZOR: Hypothesis Testing for Concept Drift Detection in Machine Learning

Data drift, that is temporal shifts in the underlying data distribution, constitutes a major threat to model reliability and long-term performance of machine learning systems operating in dynamic environments. We propose ADAZOR a drift detection algorithm focusing on the distribution of outliers relative to a learned reference, detected via a one-class SVM, which maps the original covariate stream into a sequence of binary indicators. This mapping yields a Bernoulli model, allowing the application of Z-test–based procedures for drift detection. This offers a principled, interpretable, and model-agnostic solution, facilitates data-driven maintenance decisions and provides explicit statistical guarantees, which many existing approaches lack. We compare ADAZOR against a common threshold-based technique from the literature, where drift is signaled once a monitored statistic surpasses a fixed bound. Experiments on diverse synthetic and real-world datasets with both abrupt and gradual drift indicate that our approach markedly cuts unnecessary retraining events while preserving accuracy comparable to the threshold-based approach.