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

MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High Number of Classes