Set Visualizations for Comparing and Evaluating Machine Learning Models
Abstract
Machine learning practitioners often need to compare multiple models to select the best one for their application. However, current methods of comparing models fall short because they rely on aggregate metrics that can be difficult to interpret or do not provide enough information to understand the differences between models. To better support the comparison of models, we propose set visualizations of model outputs to enable easier model-to-model comparison. We outline the requirements for using sets to compare machine learning models and demonstrate how this approach can be applied to various machine learning tasks. We also introduce SetMLVis, an interactive system that utilizes set visualizations to compare object detection models. Our evaluation shows that SetMLVis outperforms traditional visualization techniques in terms of task completion and reduces cognitive workload for users. Supplemental materials can be found at https://osf.io/afksu/?view_only=bb7f259426ad425f81d0518a38c597be.
Cite
@article{arxiv.2502.14675,
title = {Set Visualizations for Comparing and Evaluating Machine Learning Models},
author = {Liudas Panavas and Tarik Crnovrsanin and Racquel Fygenson and Eamon Conway and Derek Millard and Norbou Buchler and Cody Dunne},
journal= {arXiv preprint arXiv:2502.14675},
year = {2025}
}
Comments
Submitted to ACM Transactions on Computer-Human Interaction