Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the previous visual analytics solutions targeted only a few classes. In this paper, we present our interactive visual analytics tool, called MultiCaM-Vis, that provides \Emph{overview+detail} style parallel coordinate views and a Chord diagram for exploration and inspection of class-level miss-classification of instances. We also present results of a preliminary user study with 12 participants.

翻译：高类别多分类模型的视觉探索有助于机器学习专家识别学习阶段中问题（如实例误分类）的根源。以往的视觉分析解决方案大多仅针对少数类别。本文提出了一种名为MultiCaM-Vis的交互式视觉分析工具，该工具采用概览+细节风格的平行坐标视图和弦图，用于探索和检查类别级别的实例误分类情况。此外，我们还展示了一项包含12名参与者的初步用户研究结果。