JMLR
scikit-activeml: A Comprehensive and User-Friendly Active Learning Library
Authors
Marek Herde
Minh Tuan Pham
Daniel Kottke
Alexander Benz
Lukas Lührs
Pascal Mergard
Christoph Sandrock
Jiaying Cheng
Atal Roghman
Mehmet Müjde
Lukas Rauch
Bernhard Sick
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Sep 08, 2026
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.
Author Details
Marek Herde
AuthorMinh Tuan Pham
AuthorDaniel Kottke
AuthorAlexander Benz
AuthorLukas Lührs
AuthorPascal Mergard
AuthorChristoph Sandrock
AuthorJiaying Cheng
AuthorAtal Roghman
AuthorMehmet Müjde
AuthorLukas Rauch
AuthorBernhard Sick
AuthorCitation Information
APA Format
Marek Herde
,
Minh Tuan Pham
,
Daniel Kottke
,
Alexander Benz
,
Lukas Lührs
,
Pascal Mergard
,
Christoph Sandrock
,
Jiaying Cheng
,
Atal Roghman
,
Mehmet Müjde
,
Lukas Rauch
&
Bernhard Sick
.
scikit-activeml: A Comprehensive and User-Friendly Active Learning Library.
Journal of Machine Learning Research
.
BibTeX Format
@article{paper1599,
title = { scikit-activeml: A Comprehensive and User-Friendly Active Learning Library },
author = {
Marek Herde
and Minh Tuan Pham
and Daniel Kottke
and Alexander Benz
and Lukas Lührs
and Pascal Mergard
and Christoph Sandrock
and Jiaying Cheng
and Atal Roghman
and Mehmet Müjde
and Lukas Rauch
and Bernhard Sick
},
journal = { Journal of Machine Learning Research },
url = { https://www.jmlr.org/papers/v27/25-1999.html }
}