Trustworthy Classification through Rank-Based Conformal Prediction Sets

TL;DR

This paper introduces a new rank-based conformal prediction method that improves uncertainty quantification in classification tasks, especially with high-dimensional data and poorly calibrated models, by constructing reliable prediction sets.

Contribution

The paper presents a novel conformal prediction approach using rank-based scores, along with theoretical analysis and extensive empirical validation, advancing uncertainty quantification in classification.

Findings

Outperforms existing methods on various datasets

Achieves desired coverage with manageable set sizes

Provides reliable uncertainty quantification

Abstract

Machine learning classification tasks often benefit from predicting a set of possible labels with confidence scores to capture uncertainty. However, existing methods struggle with the high-dimensional nature of the data and the lack of well-calibrated probabilities from modern classification models. We propose a novel conformal prediction method that employs a rank-based score function suitable for classification models that predict the order of labels correctly, even if not well-calibrated. Our approach constructs prediction sets that achieve the desired coverage rate while managing their size. We provide a theoretical analysis of the expected size of the conformal prediction sets based on the rank distribution of the underlying classifier. Through extensive experiments, we demonstrate that our method outperforms existing techniques on various datasets, providing reliable uncertainty quantification. Our contributions include a novel conformal prediction method, theoretical analysis, and empirical evaluation. This work advances the practical deployment of machine learning systems by enabling reliable uncertainty quantification.