Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
This addresses the issue of difficult-to-interpret prediction sets for users of statistical machine learning methods, though it is incremental as it builds on existing CP variants.
The paper tackles the problem of interpretability in Conformal Prediction by proposing SCD-split, which uses smoothing to reduce disconnected subintervals in prediction sets, balancing interval length and subinterval count while maintaining coverage guarantees.
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.