How Much Imprecision is Enough Imprecision in my Classifier? A Practical Elicitation Procedure

arXiv:2609.33352 · cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Accuracy — 8× in this paper · explained in 1 episode
• Robustness — 5× in this paper · explained in 5 episodes
• Epistemic Uncertainty — 2× in this paper · explained in 9 episodes
• Aleatoric Uncertainty — 1× in this paper · explained in 4 episodes
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes

Sources

• Scoring Rules and Calibration for Imprecise Probabilities
• Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
• Truthful Elicitation of Imprecise Forecasts
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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