  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.
