Uncertainty quantification (UQ) is essential for creating trustworthy machine learning models. Recent years have seen a steep rise in UQ methods that can flag suspicious examples, however, it is often unclear what exactly these methods identify. In this work, we propose an assumption-light method for interpreting UQ models themselves. We introduce the confusion density matrix -- a kernel-based approximation of the misclassification density -- and use this to categorize suspicious examples identified by a given UQ method into three classes: out-of-distribution (OOD) examples, boundary (Bnd) examples, and examples in regions of high in-distribution misclassification (IDM). Through extensive experiments, we shed light on existing UQ methods and show that the cause of the uncertainty differs across models. Additionally, we show how the proposed framework can make use of the categorized examples to improve predictive performance.
翻译:确定性量化(UQ)对于创建可信赖的机器学习模式至关重要。近年来,UQ方法急剧上升,可能引出可疑的例子,但通常不清楚这些方法究竟确定了什么。在这项工作中,我们提出了一个解释UQ模型的假设光灯方法。我们引入了混杂密度矩阵 -- -- 以内核为基础的误分类密度近似值 -- -- 并以此将特定UQ方法所发现的可疑例子分为三类:分配外(OOOD)实例、边界(Bnd)实例和分布错误分类程度高(IDM)区域的实例。我们通过广泛的实验,阐明了现有的UQ方法,并表明各种模型的不确定性原因各不相同。此外,我们展示了拟议框架如何利用分类示例改进预测性。