Paper ID: 2410.19077 • Published Oct 24, 2024
Target Strangeness: A Novel Conformal Prediction Difficulty Estimator
Alexis Bose, Jonathan Ethier, Paul Guinand
TL;DR
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This paper introduces Target Strangeness, a novel difficulty estimator for
conformal prediction (CP) that offers an alternative approach for normalizing
prediction intervals (PIs). By assessing how atypical a prediction is within
the context of its nearest neighbours' target distribution, Target Strangeness
can surpass the current state-of-the-art performance. This novel difficulty
estimator is evaluated against others in the context of several conformal
regression experiments.