CalArena is a large-scale benchmark that evaluates dozens of post-hoc calibration methods using Post-Hoc Improvement (PHI) in proper scoring rules and finds that smooth functions outperform binning while dedicated multiclass methods are required in high-dimensional settings.
cc/paper_files/paper/2017/hash/ b22b257ad0519d4500539da3c8bcf4dd-Abstract
2 Pith papers cite this work. Polarity classification is still indexing.
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Calibration error tracks curvature via shared margin-dependent exponential tails; a margin-aware objective improves out-of-sample calibration across optimizers.
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CalArena: A Large-Scale Post-Hoc Calibration Benchmark
CalArena is a large-scale benchmark that evaluates dozens of post-hoc calibration methods using Post-Hoc Improvement (PHI) in proper scoring rules and finds that smooth functions outperform binning while dedicated multiclass methods are required in high-dimensional settings.
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Too Sharp, Too Sure: When Calibration Follows Curvature
Calibration error tracks curvature via shared margin-dependent exponential tails; a margin-aware objective improves out-of-sample calibration across optimizers.