Scikit-Weak: A Python Library for Weakly Supervised Machine Learning

Andrea Campagner, Julian Lienen, Eyke Hüllermeier, Davide Ciucci

Abstract: In this article we introduce and describe scikit-weak, a Python library inspired by scikit-learn and developed to provide an easy-to-use framework for dealing with weakly supervised and imprecise data learning problems, which, despite their importance in real-world settings, cannot be easily managed by existing libraries. We provide a rationale for the development of such a library, then we discuss its design and the currently implemented methods and classes, which encompass several state-of-the-art algorithms.