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Tutorial on using Conformal Predictive Systems in KNIME
Jönköping AI Lab, Department of Computing, Jönköping University, Sweden.
Redfield AB, Sweden.
Redfield AB, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-8382-0300
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2023 (English)In: Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, ML Research Press , 2023, p. 602-620Conference paper, Published paper (Refereed)
Abstract [en]

KNIME is an end-to-end software platform for data science with an open-source analytics platform for creating solutions and a commercial server solution for productionization. Conformal classification and regression have previously been implemented in KNIME. We extend the conformal prediction package with added support for conformal predictive systems, taking inspiration from the interface of the Crepes package in Python. The paper demonstrates some typical use cases for conformal predictive systems. Furthermore, the paper also illustrates how to create Mondrian conformal predictors using the KNIME implementation. All examples are publicly available, and the package is available through KNIME’s official software repositories.

Place, publisher, year, edition, pages
ML Research Press , 2023. p. 602-620
Keywords [en]
Conformal predictive systems, KNIME Analytics Platform, Mondrian conformal predictive systems, software implementation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-340792ISI: 001221733900038Scopus ID: 2-s2.0-85178664607OAI: oai:DiVA.org:kth-340792DiVA, id: diva2:1819816
Conference
12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, Limassol, Cyprus, Sep 13 2023 - Sep 15 2023
Note

QC 20231215

Available from: 2023-12-15 Created: 2023-12-15 Last updated: 2024-07-08Bibliographically approved

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Boström, Henrik

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