We present skpro, a Python framework for domain-agnostic probabilistic supervised learning. It features a scikit-learn-like general API that supports the implementation and fair comparison of both Bayesian and frequentist prediction strategies that produce conditional predictive distributions for each individual test data point. The skpro interface also supports strategy optimization through hyper-paramter tuning, model composition, ensemble methods like bagging, and workflow automation. The package and documentation are released under the BSD-3 open source license and available at GitHub.com/alan-turing-institute/skpro.

skpro: A domain-agnostic modelling framework for probabilistic supervised learning

The users who will be allowed to read the above content.

"Too Long; Didn't Read": a short sentence describing your paper

Comma separated list of author email addresses, lowercased, in the same order as above. For authors with existing OpenReview accounts, please make sure that the provided email address(es) match those listed in the author's profile.

Your authorized identity to be associated with the above content.

skpro: A domain-agnostic modelling framework for probabilistic supervised learning

Frithjof Gressmann, Franz Kiraly