Tutorial for using conformal prediction in KNIME

Tuwe Löfström, Artem Ryasik, Ulf Johansson
Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 179:4-23, 2022.

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. Redfield have previously developed nodes for conformal classification in KNIME. We introduce an extended conformal prediction package with added support for conformal regression. The conformal prediction package include class-conditional conformal classification, conformal regression and normalized conformal regression. The updated package also includes several new and updated nodes that focus on ease-of-use. This paper provide an introduction to various use cases for both simplified and advanced use as well as experiments to prove validity and showcase functionality. All examples are publicly available and the package is available through KNIME’s official software channels.

Cite this Paper

BibTeX
@InProceedings{pmlr-v179-lofstrom22a, title = {Tutorial for using conformal prediction in KNIME}, author = {L\"{o}fstr\"{o}m, Tuwe and Ryasik, Artem and Johansson, Ulf}, booktitle = {Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {4--23}, year = {2022}, editor = {Johansson, Ulf and Boström, Henrik and An Nguyen, Khuong and Luo, Zhiyuan and Carlsson, Lars}, volume = {179}, series = {Proceedings of Machine Learning Research}, month = {24--26 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v179/lofstrom22a/lofstrom22a.pdf}, url = {https://proceedings.mlr.press/v179/lofstrom22a.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. Redfield have previously developed nodes for conformal classification in KNIME. We introduce an extended conformal prediction package with added support for conformal regression. The conformal prediction package include class-conditional conformal classification, conformal regression and normalized conformal regression. The updated package also includes several new and updated nodes that focus on ease-of-use. This paper provide an introduction to various use cases for both simplified and advanced use as well as experiments to prove validity and showcase functionality. All examples are publicly available and the package is available through KNIME’s official software channels. } }
Endnote
%0 Conference Paper %T Tutorial for using conformal prediction in KNIME %A Tuwe Löfström %A Artem Ryasik %A Ulf Johansson %B Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2022 %E Ulf Johansson %E Henrik Boström %E Khuong An Nguyen %E Zhiyuan Luo %E Lars Carlsson %F pmlr-v179-lofstrom22a %I PMLR %P 4--23 %U https://proceedings.mlr.press/v179/lofstrom22a.html %V 179 %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. Redfield have previously developed nodes for conformal classification in KNIME. We introduce an extended conformal prediction package with added support for conformal regression. The conformal prediction package include class-conditional conformal classification, conformal regression and normalized conformal regression. The updated package also includes several new and updated nodes that focus on ease-of-use. This paper provide an introduction to various use cases for both simplified and advanced use as well as experiments to prove validity and showcase functionality. All examples are publicly available and the package is available through KNIME’s official software channels.
APA
Löfström, T., Ryasik, A. & Johansson, U.. (2022). Tutorial for using conformal prediction in KNIME. Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 179:4-23 Available from https://proceedings.mlr.press/v179/lofstrom22a.html.

Related Material