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Using Confusion Graphs to Understand Classifier Error
2016
Proceedings of the Workshop on Human-Computer Question Answering
unpublished
Preserved Fulltext
Understanding the nature of the errors of a machine learning system is often difficult for multiclass classification problems with a large number of classes. This is true even more so if the number of examples for each class is low. To interpret the performance of a multiclass classifier, we form a graph representing the errors, and use average-link clustering to find groups of classes which are confused with each other. We apply this idea to the QANTA question answering system (Iyyer et al.,
doi:10.18653/v1/w16-0108
fatcat:imoakfmombdd5ggcg4temjxy7q