Boxer: Interactive Comparison of Classifier Results. (18th July 2020)

• Record Type: Journal Article
• Authors: Gleicher, Michael; Barve, Aditya; Yu, Xinyi; Heimerl, Florian
• Is Part Of: Computer graphics forum. Volume 39:Number 3(2020)
• Page Start: 181
• Page End: 193
• Publication Date: 2020-07-18
• Subjects: CCS Concepts -- Human‐centered computing→ Visualization -- Visual analytics -- Information visualization; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13972
• Languages: English
• ISSNs: 0167-7055

Abstract

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.