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.
Franz J Kiraly (TU Berlin)
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