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Arxiv
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Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams
- Concept drift is extensively studied in stream learning, but the impact of model predictions on concept drift is often overlooked.
- Performative drift refers to situations where a model's predictions induce concept drift in a self-fulfilling or self-negating manner.
- A novel performative drift detection approach called CheckerBoard Performative Drift Detection (CB-PDD) is proposed.
- CB-PDD shows high efficacy, low false detection rates, and the ability to effectively detect performative drift in datasets.
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