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KindShowingImportancePrizesTopicClassifier Calibration
Claims about whether probabilistic classifiers’ predicted confidence levels agree with observed outcome frequencies, and about metrics, bounds, or methods for measuring or improving that agreement. Excludes calibration of non-classifier measurement instruments.
In weak-to-strong classification, the models’ maximum calibration errors differ by at most 2√(1−exp(−d(F_w,F_sw))).
UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →mathematicalA proposition of mathematics: true or false by proof rather than by observation. Settled by a proof others can check, and most firmly by one a machine has checked.constitution →Weak-to-Strong GeneralizationMachine Learningimportance · minorImportance 0.40, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →
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