Target Strangeness: A Novel Conformal Prediction Difficulty Estimator

TL;DR

Target Strangeness is introduced, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs) that can surpass the current state of the art performance.

Abstract

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.

Figures (2)

• Figure 1: MLPL experiments
• Figure 2: MLPL PIs result example