From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol
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Computer Science > Machine Learning
Title:From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol
Abstract:Forecasting leaderboards rank models by predictive quality, but their winners are often read as deployment-ready top-1 advice. That reading can fail when forecasts are passed through a fixed decision interface, such as an alert threshold, a top-k budget, or a switching-cost policy. We study when a forecast-side winner can be certified as deployment-actionable for a specified interface and deployed utility. We introduce a fail-closed certification protocol whose gates are sufficient evidential conditions for a strong claim: a friction-caused, non-tie, statistically supported, and recurrent deployment-side reversal. Traffic-Hourly provides a certified anchor: winners agree at zero friction, but positive switching friction makes the forecast winner deployed-suboptimal. A locked native audit tests overclaiming: across 22 verified candidates and 362 full-grid cells, 155 apparent forecast/deployment winner inversions are blocked before certification. The contribution is not a new forecaster, metric, or universal utility, but a conservative protocol for deciding when forecasting leaderboard winners should be read as deployment-actionable top-1 advice.
| Comments: | 14 pages, 2 figures, 12 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.24996 [cs.LG] |
| (or arXiv:2606.24996v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24996
arXiv-issued DOI via DataCite
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