modAL: A modular active learning framework for Python

Abstract

modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility possible, aiding the development of real-life active learning pipelines and novel algorithms as well. modAL is fully open source, hosted on GitHub at https://github.com/cosmic-cortex/modAL. To assure code quality, extensive unit tests are provided and continuous integration is applied. In addition, a detailed documentation with several tutorials are also available for ease of use. The framework is available in PyPI and distributed under the MIT license.

arXiv comment5 pages

Cited by in corpus (6)

• A near-autonomous and incremental intrusion detection system through active learning of known and unknown attacks
• Hitting the Target: Stopping Active Learning at the Cost-Based Optimum
• Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples
• Addressing practical challenges in Active Learning via a hybrid query strategy
• AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning
• Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection