Scalable Utility-Aware Multiclass Calibration

2025-10-29

AI Summary

Existing calibration evaluation methods for multiclass models struggle to simultaneously accommodate user preferences and decision-specific requirements. Method: This paper proposes a utility-aware calibration framework that incorporates user-specified utility functions into calibration error modeling—unifying top-class, inter-class, and downstream decision-sensitive calibration deviations. The framework computes calibration error efficiently and scalably from multiclass probability outputs and differentiable utility functions. Contribution/Results: Compared to conventional binning-based metrics (e.g., Expected Calibration Error), our approach significantly improves calibration robustness and practicality across diverse real-world scenarios. It is especially effective for decision tasks involving asymmetric utilities or heterogeneous class importance, offering a more flexible and semantically grounded calibration assessment paradigm for trustworthy machine learning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Decision/Utility Theory
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Ensuring that classifiers are well-calibrated, i.e., their predictions align with observed frequencies, is a minimal and fundamental requirement for classifiers to be viewed as trustworthy. Existing methods for assessing multiclass calibration often focus on specific aspects associated with prediction (e.g., top-class confidence, class-wise calibration) or utilize computationally challenging variational formulations. In this work, we study scalable emph{evaluation} of multiclass calibration. To this end, we propose utility calibration, a general framework that measures the calibration error relative to a specific utility function that encapsulates the goals or decision criteria relevant to the end user. We demonstrate how this framework can unify and re-interpret several existing calibration metrics, particularly allowing for more robust versions of the top-class and class-wise calibration metrics, and, going beyond such binarized approaches, toward assessing calibration for richer classes of downstream utilities.

Problem

• Proposes utility calibration framework for multiclass classifiers
• Unifies existing calibration metrics through utility functions
• Enables robust evaluation beyond top-class and class-wise calibration

Innovation

• Proposes utility calibration framework for multiclass evaluation
• Unifies existing metrics via user-defined utility functions
• Enables robust calibration assessment for downstream applications