activcred
activcred is an open-source software library designed to facilitate the implementation of active learning algorithms in machine learning workflows. Active learning is a semi-supervised approach where a model selectively queries the most informative unlabeled data points for labeling, aiming to improve learning efficiency by reducing the need for extensive labeled datasets. The library provides a modular framework that allows researchers and practitioners to experiment with various active learning strategies, including uncertainty sampling, query-by-committee, and density-based methods.
Developed primarily in Python, activcred integrates seamlessly with popular machine learning libraries such as scikit-learn and
activcred is particularly useful in scenarios where labeled data is scarce or expensive to obtain, such as
Documentation and tutorials are available to guide users through implementation, including examples of integrating activcred with