Version 1
: Received: 4 March 2021 / Approved: 5 March 2021 / Online: 5 March 2021 (21:14:20 CET)
How to cite:
Kottke, D.; Herde, M.; Pham Minh, T.; Benz, A.; Mergard, P.; Roghman, A.; Sandrock, C.; Sick, B. scikit-activeml: A Library and Toolbox for Active Learning Algorithms. Preprints2021, 2021030194. https://doi.org/10.20944/preprints202103.0194.v1
Kottke, D.; Herde, M.; Pham Minh, T.; Benz, A.; Mergard, P.; Roghman, A.; Sandrock, C.; Sick, B. scikit-activeml: A Library and Toolbox for Active Learning Algorithms. Preprints 2021, 2021030194. https://doi.org/10.20944/preprints202103.0194.v1
Kottke, D.; Herde, M.; Pham Minh, T.; Benz, A.; Mergard, P.; Roghman, A.; Sandrock, C.; Sick, B. scikit-activeml: A Library and Toolbox for Active Learning Algorithms. Preprints2021, 2021030194. https://doi.org/10.20944/preprints202103.0194.v1
APA Style
Kottke, D., Herde, M., Pham Minh, T., Benz, A., Mergard, P., Roghman, A., Sandrock, C., & Sick, B. (2021). scikit-activeml: A Library and Toolbox for Active Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202103.0194.v1
Chicago/Turabian Style
Kottke, D., Christoph Sandrock and Bernhard Sick. 2021 "scikit-activeml: A Library and Toolbox for Active Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202103.0194.v1
Abstract
Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.
Active Learning, Classification, Machine Learning, Python, Github, Repository, Open Source
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.