Selective Prediction
Definition
Selective prediction allows a system to abstain from making a decision in certain cases.
A prediction method with a rejection option: The system only outputs a prediction for a selected subset of cases and abstains otherwise. Errors or risk are evaluated together with coverage.
Simply explained
The model answers suitable cases and forwards uncertain cases for further review.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A quality model decides only with high confidence and refers borderline cases to experts.
Limits and pitfalls
- Treating raw softmax values as calibrated confidence, reporting only errors among accepted cases, hiding coverage, or excluding escalated cases from overall impact.
Origin & history
Selective classification combines decision theory, confidence estimation, and abstention.
Use in mid-sized companies
- Possible example: A quality model decides only with high confidence and refers borderline cases to experts.
Frequently asked questions
What does selective prediction mean?
A prediction method with a rejection option: The system only outputs a prediction for a selected subset of cases and abstains otherwise. Errors or risk are evaluated together with coverage. What mistakes should be avoided? Treating raw softmax values as calibrated confidence, reporting only errors among accepted cases, hiding coverage, or excluding escalated cases from overall impact.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
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