Beyond Prediction-Set Size: Quantifying Conformal Uncertainty through Credal Geometry
Read the original on arXiv Machine Learning →The paper extends conformal prediction by introducing a geometric view of uncertainty through credal sets, which are families of probability distributions compatible with CP regions. It provides analytical characterisations and efficient algorithms for common credal uncertainty measures, reducing complexity from exponential to linear after sorting p-values. The authors also unify these measures with classical conformal summaries using Tsallis entropy penalties, prove an impossibility theorem for split-conformal regression, and demonstrate through experiments that the new summaries recover ranking information lost by prediction-set size, enabling finer-grained uncertainty assessment.
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