arXiv:2605.30188v1 Announce Type: new Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated. Post-hoc calibration provides a simple and widely used solution, but the large number of proposed methods, combined with small-scale and inconsistent evaluations, makes it difficult to determine which approaches are truly effective in practice. We introduce a large-scale, standardized benchmark for post-hoc calibration, covering nearly 2000 experiments across tabular and computer vision tasks, including binary, mu
The proliferation of machine learning models across diverse applications necessitates a robust and standardized approach to model reliability, making post-hoc calibration a critical area of focus.
Reliable probability estimates are fundamental for deploying AI systems safely and effectively in critical applications, and improved calibration benchmarks accelerate this development.
The introduction of CalArena provides a standardized, large-scale benchmark for evaluating post-hoc calibration methods, potentially leading to more consistent and effective calibration techniques.
- · AI developers
- · Machine learning researchers
- · Industries relying on AI predictions
- · Developers of poorly calibrated AI models
AI models will become more trustworthy and reliable in their probabilistic outputs.
Increased trust in AI's probabilistic judgments could lead to wider adoption in high-stakes fields like healthcare and finance.
Improved model calibration may lead to a more nuanced understanding and control over AI system behavior, fostering more responsible AI development.
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Read at arXiv cs.LG