scikit-activeml: A Comprehensive and User-friendly Active Learning Library
De autoria de Marek Herde, Minh Tuan Pham, Daniel Kottke, Alexander Benz, Lukas Lührs, Pascal Mergard, Christoph Sandrock, Jiaying Cheng, Atal Roghman, Mehmet Müjde, Lukas Rauch e Bernhard Sick
Publicado
Servidor
Preprints.org
DOI
10.20944/preprints202507.0252.v1
scikit-activeml is a user-friendly open-source Python library for active learning on top of scikit-learn. Included are implementations of a large collection of query strategies, models, and visualization tools in pool- and stream-based active learning for classification or regression tasks with single or multiple annotators. The flexible design of the active learning cycle enables individual adaptations to a variety of learning scenarios. Our source code with comprehensive documentation is available at https://scikit-activeml.github.io.
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