  Hyperbox-based machine learning algorithms are an important and popular
branch of machine learning in the construction of classifiers using fuzzy sets
and logic theory and neural network architectures. This type of learning is
characterised by many strong points of modern predictors such as a high
scalability, explainability, online adaptation, effective learning from a small
amount of data, native ability to deal with missing data and accommodating new
classes. Nevertheless, there is no comprehensive existing package for
hyperbox-based machine learning which can serve as a benchmark for research and
allow non-expert users to apply these algorithms easily. hyperbox-brain is an
open-source Python library implementing the leading hyperbox-based machine
learning algorithms. This library exposes a unified API which closely follows
and is compatible with the renowned scikit-learn and numpy toolboxes. The
library may be installed from Python Package Index (PyPI) and the conda package
manager and is distributed under the GPL-3 license. The source code,
documentation, detailed tutorials, and the full descriptions of the API are
available at https://uts-caslab.github.io/hyperbox-brain.
