Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based methods and publicly available software to address it. In this paper, we present a model-agnostic method of ordinal classification, which can apply any non-ordinal classification method in an ordinal fashion. We also provide an open-source implementation of these algorithms, in the form of a Python package. We apply these models on multiple real-world datasets to show their performance across domains. We show that they often outperform non-ordinal classification methods, especially when the number of datapoints is relatively small or when there are many classes of outcomes. This work, including the developed software, facilitates the use of modern, more powerful machine learning algorithms to handle ordinal data.

翻译：序数数据在临床及其他领域广泛存在，但目前既缺乏基于现代机器学习的方法，也缺少公开可用的处理软件。本文提出一种与模型无关的序数分类方法，能够以序数方式应用任何非序数分类方法。同时，我们以Python软件包的形式提供了这些算法的开源实现。通过在多个真实数据集上应用这些模型，我们展示了其在跨领域任务中的性能表现。实验表明，这些方法通常优于非序数分类方法，特别是在数据点数量相对较少或结果类别较多的情况下。本研究成果（包括所开发的软件）为使用更强大的现代机器学习算法处理序数数据提供了便利。