Some requests arrive with incomplete data.
The model still returns predictions.
But quality is unpredictable.
I need a safer approach?
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Some requests arrive with incomplete data.
The model still returns predictions.
But quality is unpredictable.
I need a safer approach?
Missing features should be handled explicitly, not implicitly.
Define clear defaults or fallback behavior during training and inference. Consider rejecting predictions when critical features are missing.
Monitor missing-value rates in production to catch upstream issues early.
Common mistakes include:
Relying on framework defaults
Ignoring missing feature trends
Treating all features as optional
The takeaway is that silent assumptions create silent failures.