On Optimal Data Splitting for Split Conformal Prediction
Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample coverage guarantees. The statistical efficiency of these intervals depends critically on how the data are split into training and calibration samples. Despite its practical importance, a principled characterization of the training-calibration split that minimizes prediction interval length while maintaining coverage has remained largely unresolved. In this paper, we develop a theoretical framework for optimal data splitting in split conformal prediction. We first analyze the pro...
| Authors | Sayan Das, Bahram Yaghooti, Todd A. Kuffner et al. |
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| Primary category | math.ST |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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