2024-09-10-bostrom24a.md

title	booktitle	year	volume	series	month	publisher	pdf	url	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Conformal Prediction in Python with crepes	Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	2024	230	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v230/main/assets/bostrom24a/bostrom24a.pdf	https://proceedings.mlr.press/v230/bostrom24a.html	\verb|crepes| is a Python package for conformal prediction, which has been extended in several ways since its introduction. While the original version of the package focused on conformal regressors and predictive systems, the current version also includes conformal classifiers. New classes and methods for computing non-conformity scores and Mondrian categories have also been incorporated. Moreover, the package has been extended to allow for seamless embedding of classifiers and regressors in the conformal prediction framework; instead of generating conformal predictors that are separate from the learners, the latter can now be equipped with specific prediction methods that in addition to providing point predictions also can generate p-values, prediction sets and intervals, as well as conformal predictive distributions. Extensive documentation for the package has furthermore been developed. In this paper, these extensions are described, as implemented in \verb|crepes|, version 0.7.0.	inproceedings	2640-3498	bostrom24a	Conformal Prediction in Python with crepes	236	249	236-249	236	false	Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars	given family Simone Vantini given family Matteo Fontana given family Aldo Solari given family Henrik Boström given family Lars Carlsson	Bostr\"{o}m, Henrik	given family Henrik Boström	2024-09-10		Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	inproceedings	date-parts 2024 9 10