Visual Analytics for Hierarchical and Multi-Output Confusion Matrix

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Machine Learning

Human-Computer Interaction

Artificial Intelligence

Visual Analytics for Hierarchical and Multi-Output Confusion Matrix

Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

Conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchicaland multi-output labels.

To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions.

Based on this algebra, we develop neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical andmulti-output confusion matrices, visualize derived metrics, renormalize confusions, and share matrix specifications.

Finally, we demonstrate neo s utility with three case studies that help people better understand model performance and reveal hidden confusions.

Authors

Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel

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