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.
Description
modAL: A modular active learning framework for Python
%0 Generic
%1 danka2018modal
%A Danka, Tivadar
%A Horvath, Peter
%D 2018
%K machinelearn
%T modAL: A modular active learning framework for Python
%U http://arxiv.org/abs/1805.00979
%X 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.
@misc{danka2018modal,
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.},
added-at = {2023-05-21T11:36:56.000+0200},
author = {Danka, Tivadar and Horvath, Peter},
biburl = {https://www.bibsonomy.org/bibtex/2a6e61ecaa4c3426b7befcb0094c36f80/cmcneile},
description = {modAL: A modular active learning framework for Python},
interhash = {b05747d7527453b7ae0756e909a12150},
intrahash = {a6e61ecaa4c3426b7befcb0094c36f80},
keywords = {machinelearn},
note = {cite arxiv:1805.00979Comment: 5 pages},
timestamp = {2023-05-21T11:36:56.000+0200},
title = {modAL: A modular active learning framework for Python},
url = {http://arxiv.org/abs/1805.00979},
year = 2018
}