  The democratization of Data Mining has been widely successful thanks in part
to powerful and easy-to-use Machine Learning libraries. These libraries have
been particularly tailored to tackle Supervised Learning. However, strong
supervision signals are scarce in practice, and practitioners must resort to
weak supervision. In addition to weaknesses of supervision, dataset shifts are
another kind of phenomenon that occurs when deploying machine learning models
in the real world. That is why Biquality Learning has been proposed as a
machine learning framework to design algorithms capable of handling multiple
weaknesses of supervision and dataset shifts without assumptions on their
nature and level by relying on the availability of a small trusted dataset
composed of cleanly labeled and representative samples. Thus we propose
biquality-learn: a Python library for Biquality Learning with an intuitive and
consistent API to learn machine learning models from biquality data, with
well-proven algorithms, accessible and easy to use for everyone, and enabling
researchers to experiment in a reproducible way on biquality data.

Machine Learning