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The notion of concept drift refers to the phenomenon that the distribution,which is underlying the observed data, changes over time; as a consequencemachine learning models may become inaccurate and need adjustment. Manyunsupervised approaches for drift detection rely on measuring the discrepancybetween the sample distributions of two time windows. This may be donedirectly, after some preprocessing (feature extraction, embedding into a latentspace, etc.), or with respect to inferred features (mean, variance, conditionalprobabilities etc.). Most drift detection methods can be distinguished in whatmetric they use, how this metric is estimated, and how the decision thresholdis found. In this paper, we analyze structural properties of the drift inducedsignals in the context of different metrics. We compare different types ofestimators and metrics theoretically and empirically and investigate therelevance of the single metric components. In addition, we propose new choicesand demonstrate their suitability in several experiments.

Suitability of Different Metric Choices for Concept Drift Detection