We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we provide two examples of classifier comparisons on samples of standard benchmark data sets. Our results demonstrate promisingly the wide variety of different analysis approaches based on ufg methods. Furthermore, the examples outline that our approach differs substantially from existing benchmarking approaches, and thus adds a new perspective to the vivid debate on classifier comparison.

我们提出了一个通过深度函数对部分排序集进行描述性分析的框架。具体地，我们引入了一种适用于所有部分排序的著名单形深度的改进方法，即不相交统一（ufg）深度，并利用我们的ufg深度对基于多维性能度量的机器学习算法进行比较。通过在标准基准数据集的样本上提供两个分类器比较的例子，我们的结果有望展示基于ufg方法的不同分析方法的广泛应用。此外，这些例子概述了我们的方法与现有基准方法的明显差异，从而为关于分类器比较的活跃讨论增加了新的视角。

通过无交集通用深度比较机器学习算法