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An Interactive Visual Tool to Enhance Understanding of Random Forest Predictions

Gurung, Ram B.; Lindgren, Tony; Boström, Henrik

Random forests are known to provide accurate predictions, but the predictions are not easy to understand. In order to provide support for understanding such predictions, an interactive visual tool has been developed. The tool can be used to manipulate selected features to explore “what-if” scenarios. It exploits the internal structure of decision trees in a trained forest model and presents this information as interactive plots and charts. In addition, the tool presents a simple decision rule as an explanation for the prediction. It also presents the recommendation for reassignments of feature values of the example that leads to change in the prediction to a preferred class. An evaluation of the tool was undertaken in a large truck manufacturing company, targeting the fault prediction of a selected component in trucks. A set of domain experts were invited to use the tool and provide feedback in post-task interviews. The result of this investigation suggests that the tool indeed may aid in understanding the predictions of a random forest, and also allows for gaining new insights.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000098011/08
Veröffentlicht am 09.03.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000130424
Erschienen in Archives of Data Science, Series A (Online First)
Band 6
Heft 1
Seiten P08, 17 S. online
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