Typed Decision Models under Prevalence Shift: Decision Training, Calibration, and Implicit Priors
Typed decision models (TDMs) return a probability over caller-defined options, and Jev is described by its vendor as calibrated. But in deployment class frequencies change, so calibration measured on a fixed test set need not hold where thresholds are applied. Existing evaluations leave two gaps: the gains of vendor decision training have not been separated from those of the backbone under an identical readout, and TDMs have been tested only at the test set's class distribution. We ask whether decision training improves a model over its own backbone, and whether a TDM's calibration and thresholds withstand prevalence (label) shift as well as a fine-tuned classifier's. We compare three TDM...
| Authors | Abdul Rahman Iqbal, Muhammad Fawad Nasim, Amir Saleem |
|---|---|
| Primary category | cs.LG |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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