Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

scikit-activeml: A Library and Toolbox for Active Learning Algorithms

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. Preprints 2021, 2021030194 (doi: 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 (doi: 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.

Supplementary and Associated Material

Keywords

Active Learning, Classification, Machine Learning, Python, Github, Repository, Open Source

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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