Paper ID: 2410.19077

Target Strangeness: A Novel Conformal Prediction Difficulty Estimator

Alexis Bose, Jonathan Ethier, Paul Guinand

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.

Submitted: Oct 24, 2024