Conformal prediction and its integration within visual analytics toolbox

Tomaž Hočevar, Blaž Zupan
Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 152:286-293, 2021.

Abstract

Conformal prediction is a machine learning approach to report on the reliability of predictive models when applied to new cases. Machine learning techniques are gaining in complexity, and assessing their reliability may be an essential part of explaining the inner workings of predictive models. For practical purposes and dissemination of conformal prediction techniques, we must include these within easily accessible toolboxes. In machine learning, a significant subset of such toolboxes is those that use work flows and visual programming. Here, we report on an example of such a toolbox, Python implementation of conformal prediction library, and our initial efforts and ideas to democratize conformal prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v152-hocevar21a, title = {Conformal prediction and its integration within visual analytics toolbox}, author = {Ho\v{c}evar, Toma\v{z} and Zupan, Bla\v{z}}, booktitle = {Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications}, pages = {286--293}, year = {2021}, editor = {Carlsson, Lars and Luo, Zhiyuan and Cherubin, Giovanni and An Nguyen, Khuong}, volume = {152}, series = {Proceedings of Machine Learning Research}, month = {08--10 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v152/hocevar21a/hocevar21a.pdf}, url = {https://proceedings.mlr.press/v152/hocevar21a.html}, abstract = {Conformal prediction is a machine learning approach to report on the reliability of predictive models when applied to new cases. Machine learning techniques are gaining in complexity, and assessing their reliability may be an essential part of explaining the inner workings of predictive models. For practical purposes and dissemination of conformal prediction techniques, we must include these within easily accessible toolboxes. In machine learning, a significant subset of such toolboxes is those that use work flows and visual programming. Here, we report on an example of such a toolbox, Python implementation of conformal prediction library, and our initial efforts and ideas to democratize conformal prediction.} }
Endnote
%0 Conference Paper %T Conformal prediction and its integration within visual analytics toolbox %A Tomaž Hočevar %A Blaž Zupan %B Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2021 %E Lars Carlsson %E Zhiyuan Luo %E Giovanni Cherubin %E Khuong An Nguyen %F pmlr-v152-hocevar21a %I PMLR %P 286--293 %U https://proceedings.mlr.press/v152/hocevar21a.html %V 152 %X Conformal prediction is a machine learning approach to report on the reliability of predictive models when applied to new cases. Machine learning techniques are gaining in complexity, and assessing their reliability may be an essential part of explaining the inner workings of predictive models. For practical purposes and dissemination of conformal prediction techniques, we must include these within easily accessible toolboxes. In machine learning, a significant subset of such toolboxes is those that use work flows and visual programming. Here, we report on an example of such a toolbox, Python implementation of conformal prediction library, and our initial efforts and ideas to democratize conformal prediction.
APA
Hočevar, T. & Zupan, B.. (2021). Conformal prediction and its integration within visual analytics toolbox. Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 152:286-293 Available from https://proceedings.mlr.press/v152/hocevar21a.html.

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