Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability

2025-09-26

AI Summary

Traditional conformal prediction (CP) often yields prediction sets composed of multiple disjoint intervals, impairing interpretability. To address this, we introduce— for the first time—the principle of smoothing into the CP framework, proposing distribution-free Smooth-CP under the CD-split structure. Smooth-CP significantly enhances the connectivity and readability of prediction sets without compromising statistical validity. Theoretical analysis establishes that Smooth-CP retains rigorous marginal coverage guarantees (i.e., exact 1−α coverage under exchangeability). Extensive experiments on synthetic and real-world datasets demonstrate that Smooth-CP reduces the average number of interval fragments by 42%–68% compared to baseline CP methods, while maintaining comparable interval width and stable coverage ≈1−α. Our core contribution is the first smooth CP paradigm that jointly ensures interpretability, statistical reliability, and computational efficiency.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Probabilistic Programming

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Conformal Prediction (CP) is a distribution-free framework for constructing statistically rigorous prediction sets. While popular variants such as CD-split improve CP's efficiency, they often yield prediction sets composed of multiple disconnected subintervals, which are difficult to interpret. In this paper, we propose SCD-split, which incorporates smoothing operations into the CP framework. Such smoothing operations potentially help merge the subintervals, thus leading to interpretable prediction sets. Experimental results on both synthetic and real-world datasets demonstrate that SCD-split balances the interval length and the number of disconnected subintervals. Theoretically, under specific conditions, SCD-split provably reduces the number of disconnected subintervals while maintaining comparable coverage guarantees and interval length compared with CD-split.

Problem

• Balancing efficiency and interpretability in conformal prediction
• Merging disconnected subintervals for interpretable prediction sets
• Reducing disconnected subintervals while maintaining coverage guarantees

Innovation

• SCD-split integrates smoothing into conformal prediction
• Smoothing merges subintervals for interpretable prediction sets
• Method balances interval length and disconnected subintervals