Overall metrics look acceptable.
But certain users receive poor predictions.
The issue isn’t uniform. It’s hard to detect early?
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Overall metrics look acceptable.
But certain users receive poor predictions.
The issue isn’t uniform. It’s hard to detect early?
Segment-specific degradation often indicates biased or underrepresented training data.
Certain user groups may appear rarely in training but frequently in production. As a result, the model generalizes poorly for them.
Break down metrics by meaningful segments such as geography, device type, or behavior patterns. This often reveals hidden weaknesses.
Consider targeted data collection or separate models for high-impact segments.The takeaway is that averages hide important failures