biquality-learn: a Python library for Biquality Learning

TL;DR

biquality-learn is a Python library that facilitates machine learning from datasets with weak supervision and dataset shifts by leveraging a small trusted dataset, thus addressing real-world data challenges.

Contribution

It introduces an easy-to-use Python library with algorithms for biquality learning, enabling robust model training under supervision weaknesses and dataset shifts.

Findings

Provides a consistent API for biquality learning

Includes well-proven algorithms for handling weak supervision

Enables reproducible research on biquality data

Abstract

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.