How Data Teams Handle Concept Drift

In this episode of The Data Science Podcast with Fexingo, Lucas and Luna dive into the subtle but critical issue of concept drift. While feature drift is well understood, concept drift represents a fundamental shift in the relationship between input data and target variables over time. Using examples from e-commerce recommendation engines and credit scoring models, we explore why model accuracy can silently degrade even when input distributions remain stable. The hosts discuss detection strategies, retraining triggers, and the human element of interpreting model performance in a changing world.

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