标题： 校准足够 显示英文标题

标题： Calibrating sufficiently

摘要： 当概率分类器被训练和校准时，校准损失中的所谓分组损失部分很容易被忽视。 分组损失指的是可观测信息与校准过程中实际利用的信息之间的差距。 我们研究了分组损失与充分性概念之间的关系，确定了同向性作为充分性的一个有用标准。 我们重新审视Langford & Zadrozny (2005)的探测约简方法，并发现它产生了一个减少分组损失的概率分类器估计器。 最后，我们讨论了Brier曲线作为支持概率分类器训练和“充分”校准的工具。 显示英文摘要

摘要： When probabilistic classifiers are trained and calibrated, the so-called grouping loss component of the calibration loss can easily be overlooked. Grouping loss refers to the gap between observable information and information actually exploited in the calibration exercise. We investigate the relation between grouping loss and the concept of sufficiency, identifying comonotonicity as a useful criterion for sufficiency. We revisit the probing reduction approach of Langford & Zadrozny (2005) and find that it produces an estimator of probabilistic classifiers that reduces grouping loss. Finally, we discuss Brier curves as tools to support training and 'sufficient' calibration of probabilistic classifiers.