Suite of tools for analysing the independence between training and evaluation biosequence datasets and to generate new generalisation-evaluating hold-out partitions
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# Hestia Independent evaluation set construction for trustworthy ML models in biochemistry
<a href=”https://ibm.github.io/Hestia-OOD/”><img alt=”Tutorials” src=”https://img.shields.io/badge/docs-tutorials-green” /></a> <a href=”https://github.com/IBM/Hestia-OOD/blob/main/LICENSE”><img alt=”GitHub” src=”https://img.shields.io/github/license/IBM/Hestia-OOD” /></a> <a href=”https://pypi.org/project/hestia-ood/”><img src=”https://img.shields.io/pypi/v/hestia-ood” /></a> <a href=”https://pypi.org/project/hestia-ood/”><img src=”https://img.shields.io/pypi/dm/hestia-ood” /></a>
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