Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a user-chosen probability. In many classification problems, we would like to obtain a stronger guarantee -- that for test points of a specific class, the prediction set contains the true label with the same user-chosen probability. Existing conformal prediction methods do not work well when there is a limited amount of labeled data per class, as is often the case in real applications where the number of classes is large. We propose a method called clustered conformal prediction, which clusters together classes that have "similar" conformal scores and then performs conformal prediction at the cluster level. Based on empirical evaluation across four image data sets with many (up to 1000) classes, we find that clustered conformal typically outperforms existing methods in terms of class-conditional coverage and set size metrics.

本文提出了一种称为聚类置信预测的方法，该方法通过聚类类，将具有“相似”信心得分的类聚合在一起，然后在聚类水平上执行置信预测，进行分类问题中的更强的置信度验证, 应用于多分类图像数据集中表现更加优秀。

带有许多类别的类条件拟合预测