PREreviews of “scikit-activeml: A Comprehensive and User-friendly Active Learning Library”

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scikit-activeml: A Comprehensive and User-friendly Active Learning Library

by Marek Herde, Minh Tuan Pham, Daniel Kottke, Alexander Benz, Lukas Lührs, Pascal Mergard, Christoph Sandrock, Jiaying Cheng, Atal Roghman, Mehmet Müjde, Lukas Rauch, and Bernhard Sick

Posted: July 4, 2025
Server: Preprints.org
DOI: 10.20944/preprints202507.0252.v1

Abstract

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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