A Topological-Framework to Improve Analysis of Machine Learning Model Performance

• URL: http://arxiv.org/abs/2107.04714v1
• Date: Fri, 9 Jul 2021 23:11:13 GMT
• Title: A Topological-Framework to Improve Analysis of Machine Learning Model Performance
• Authors: Henry Kvinge, Colby Wight, Sarah Akers, Scott Howland, Woongjo Choi, Xiaolong Ma, Luke Gosink, Elizabeth Jurrus, Keerti Kappagantula, Tegan H. Emerson
• Abstract summary: We propose a framework for evaluating machine learning models in which a dataset is treated as a "space" on which a model operates. We describe a topological data structure, presheaves, which offer a convenient way to store and analyze model performance between different subpopulations.
• Score: 5.3893373617126565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model performance has become increasingly problematic. This is particularly true in real-world scenarios where understanding model failure on certain subpopulations of the data is of critical importance. In this paper we propose a topological framework for evaluating machine learning models in which a dataset is treated as a "space" on which a model operates. This provides us with a principled way to organize information about model performance at both the global level (over the entire test set) and also the local level (on specific subpopulations). Finally, we describe a topological data structure, presheaves, which offer a convenient way to store and analyze model performance between different subpopulations.

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