Conformalized Ordinal Classification with Marginal and Conditional Coverage

arXiv:2404.16610v1 Announce Type: cross
Abstract: Conformal prediction is a general distribution-free approach for constructing prediction sets combined with any machine learning algorithm that achieve valid marginal or conditional coverage in finite samples. Ordinal classification is common in real applications where the target variable has natural ordering among the class labels. In this paper, we discuss constructing distribution-free prediction sets for such ordinal classification problems by leveraging the ideas of conformal prediction and multiple testing with FWER control. Newer conformal prediction …

abstract algorithm applications arxiv class classification coverage distribution free general labels machine machine learning natural ordinal prediction samples stat.me stat.ml type