Treat the missingness as systematic: Which conclusion is decisive?
Concentrated missing labels can make aggregate accuracy unrepresentative of small-business documents.
The question
A financial-document classifier is evaluated on a random held-out sample and reports strong accuracy. Thirty percent of documents lack labels, and the missing labels are concentrated among small-business applicants whose forms differ from the rest. Which conclusion is decisive?
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- Increase the random test sample without repairing the label gap.A larger random sample can retain the same systematic missingness and does not establish correct outcomes for affected documents.
- Treat the missingness as systematic. ✓Concentrated missing labels indicate structured missingness, so aggregate accuracy may not represent small-business documents.
- Assume unlabeled documents resemble the labeled population.The stated concentration among small-business applicants contradicts an assumption that unlabeled documents are representative.
- Use synthetic labels as ground truth for the affected documents.Synthetic labels may reproduce bias or errors and do not independently validate outcomes for the affected applicants.
The trap
Ask whether missing labels are random across the intended population before trusting an aggregate metric. How to remember it
Concentrated missing labels can make aggregate accuracy unrepresentative of small-business documents.
How many of these would you get right?
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