What is Model Drift? | Stanford HAI
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What is Model Drift?

Model Drift occurs when a machine learning model's performance degrades over time because the real-world data it encounters has changed from the data it was originally trained on. This happens when patterns, relationships, or distributions in the incoming data shift due to changes in user behavior, market conditions, seasonality, or other external factors. For example, a fraud detection model trained before the pandemic might perform poorly afterward because shopping patterns fundamentally changed, or a recommendation system might drift as user preferences evolve. 

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Model drift mentioned at Stanford HAI

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