Visualizing Prediction Provenance in Regression Random Forests

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dc.contributor.authorMédoc, Nicolasen_US
dc.contributor.authorCiorna, Vasileen_US
dc.contributor.authorPetry, Franken_US
dc.contributor.authorGhoniem, Mohammaden_US
dc.contributor.editorKrone, Michaelen_US
dc.contributor.editorLenti, Simoneen_US
dc.contributor.editorSchmidt, Johannaen_US
dc.date.accessioned2022-06-02T15:29:12Z
dc.date.available2022-06-02T15:29:12Z
dc.date.issued2022
dc.description.abstractRandom forest models are widely used in many application domains due to their performance and the fact that their constituent decision trees carry clear decision rules. Yet, the provenance of the predictions made by an entire forest is complex to grasp, which motivates application domain experts to adopt black-box testing strategies. We propose in this paper a coordinated multiple view system allowing to shed more light on prediction provenance, uncertainty and error in terms of bias and variance at the global model scale or at the local scale of decision paths and individual instances.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2022 - Posters
dc.identifier.doi10.2312/evp.20221124
dc.identifier.isbn978-3-03868-185-4
dc.identifier.pages75-77
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20221124
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evp20221124
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Visualization; Computing methodologies --> Classification and regression trees
dc.subjectHuman centered computing
dc.subjectVisualization
dc.subjectComputing methodologies
dc.subjectClassification and regression trees
dc.titleVisualizing Prediction Provenance in Regression Random Forestsen_US
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