Abstract
The paper presents the ViperCharts web-based platform for visual performance evaluation of classification, prediction, and information retrieval algorithms. The platform enables to create interactive charts for easy and intuitive evaluation of performance results. It includes standard visualizations and extends them by offering alternative evaluation methods like F-isolines, and by establishing relations between corresponding presentations like Precision-Recall and ROC curves. Additionally, the interactive performance charts can be saved, exported to several formats, and shared via unique web addresses. A web API to the service is also available.
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Sluban, B., Lavrač, N. (2013). ViperCharts: Visual Performance Evaluation Platform. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_47
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DOI: https://doi.org/10.1007/978-3-642-40994-3_47
Publisher Name: Springer, Berlin, Heidelberg
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