Tutorial on using Conformal Predictive Systems in KNIME

Tuwe Lofstrom, Alexander Bondaletov, Artem Ryasik, Henrik Bostrom, Ulf Johansson
Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 204:602-620, 2023.

Abstract

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 is1 available through KNIME’s official software repositories.

Cite this Paper

BibTeX
@InProceedings{pmlr-v204-lofstrom23a, title = {Tutorial on using Conformal Predictive Systems in KNIME}, author = {Lofstrom, Tuwe and Bondaletov, Alexander and Ryasik, Artem and Bostrom, Henrik and Johansson, Ulf}, booktitle = {Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {602--620}, year = {2023}, editor = {Papadopoulos, Harris and Nguyen, Khuong An and Boström, Henrik and Carlsson, Lars}, volume = {204}, series = {Proceedings of Machine Learning Research}, month = {13--15 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v204/lofstrom23a/lofstrom23a.pdf}, url = {https://proceedings.mlr.press/v204/lofstrom23a.html}, abstract = {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 is1 available through KNIME’s official software repositories.} }
Endnote
%0 Conference Paper %T Tutorial on using Conformal Predictive Systems in KNIME %A Tuwe Lofstrom %A Alexander Bondaletov %A Artem Ryasik %A Henrik Bostrom %A Ulf Johansson %B Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2023 %E Harris Papadopoulos %E Khuong An Nguyen %E Henrik Boström %E Lars Carlsson %F pmlr-v204-lofstrom23a %I PMLR %P 602--620 %U https://proceedings.mlr.press/v204/lofstrom23a.html %V 204 %X 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 is1 available through KNIME’s official software repositories.
APA
Lofstrom, T., Bondaletov, A., Ryasik, A., Bostrom, H. & Johansson, U.. (2023). Tutorial on using Conformal Predictive Systems in KNIME. Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 204:602-620 Available from https://proceedings.mlr.press/v204/lofstrom23a.html.

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