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

• URL: http://arxiv.org/abs/2110.12536v1
• Date: Sun, 24 Oct 2021 21:55:20 GMT
• Title: Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
• Authors: Jochen G\"ortler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel
• Abstract summary: The confusion matrix is a ubiquitous visualization for helping people evaluate machine learning models. We find that conventional confusion matrices do not support more complex data-structures, such as hierarchical and multi-output labels. We develop Neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical and multi-output confusion matrices.
• Score: 25.336125962529692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at a large technology company and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and 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 and multi-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.

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