Boxer: Interactive Comparison of Classifier Results

• URL: http://arxiv.org/abs/2004.07964v1
• Date: Thu, 16 Apr 2020 21:05:34 GMT
• Title: Boxer: Interactive Comparison of Classifier Results
• Authors: Michael Gleicher, Aditya Barve, Xinyi Yu, Florian Heimerl
• Abstract summary: Boxer is a system to enable machine learning comparisons. It allows the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.
• Score: 9.957660146705745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.

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