Hierarchical Conformal Classification

Conformal prediction (CP) provides reliable uncertainty quantification by generating prediction sets with finite-sample coverage guarantees, yet it typically ignores hierarchical relationships between classes. We introduce Hierarchical Conformal Classification (HCC), a framework that integrates class hierarchies into the structure and semantics of prediction sets. HCC uses a constrained optimization problem formulation to produce prediction sets composed of nodes at various hierarchy levels while maintaining rigorous coverage guarantees. To ensure computational efficiency, we prove that a restricted subset of well-structured candidate solutions is sufficient to maintain both optimality and coverage. Empirical evaluations across audio, image, and text benchmarks, alongside a user study, demonstrate that HCC outperforms state-of-the-art methods and that hierarchical prediction sets align better with human preferences.