Conformal Tradeoffs: Operational Profiles Beyond Coverage
Petrus H. Zwart

TL;DR
This paper introduces a new method called Small-Sample Beta Correction (SSBC) for conformal prediction that provides finite-sample coverage guarantees and operational profile insights beyond traditional coverage metrics, enabling better deployment decision-making.
Contribution
The paper proposes SSBC, a novel approach that maps user requests to coverage guarantees and introduces an audit-based framework for operational KPI estimation in conformal prediction.
Findings
SSBC provides finite-sample coverage semantics for deployed rules.
Audit-based summaries accurately estimate operational KPIs.
Simulations and case studies validate the approach.
Abstract
Conformal prediction gives exact finite-sample coverage guarantees under exchangeability, but deployed systems are judged by more than coverage alone. For a fixed calibrated rule reused over a finite operational window, stakeholders also care about deployment-facing quantities such as commitment frequency, deferral, and decisive error exposure. These are not determined by coverage: calibration choices with similar coverage can still induce materially different operational profiles. We study this characterization gap in a scoped setting: binary split conformal prediction under exchangeability with a fixed deployed rule. We introduce the Small-Sample Beta Correction (SSBC) which gives finite-sample coverage semantics for the deployed rule: it inverts the Beta/Beta--Binomial law governing calibration-conditional coverage to map a user request to the least…
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Taxonomy
TopicsScientific Computing and Data Management · Adversarial Robustness in Machine Learning · Machine Learning in Materials Science
