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dc.contributor.authorGleicher, Michaelen_US
dc.contributor.authorBarve, Adityaen_US
dc.contributor.authorYu, Xinyien_US
dc.contributor.authorHeimerl, Florianen_US
dc.contributor.editorViola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatianaen_US
dc.description.abstractMachine 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectInformation visualization
dc.titleBoxer: Interactive Comparison of Classifier Resultsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersMachine Learning

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  • 39-Issue 3
    EuroVis 2020 - Conference Proceedings

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License