| Name | julearn JSON |
| Version |
0.3.4
JSON |
| download |
| home_page | None |
| Summary | Juelich Machine Learning Library |
| upload_time | 2024-10-17 14:13:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | AGPL-3.0-only |
| keywords |
machine-learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# julearn
[](https://anaconda.org/conda-forge/julearn)
[](https://github.com/charliermarsh/ruff)
[](https://github.com/pre-commit/pre-commit)
## About
The Forschungszentrum Jülich Machine Learning Library
Check our full documentation here: https://juaml.github.io/julearn/index.html
It is currently being developed and maintained at the [Applied Machine Learning](https://www.fz-juelich.de/en/inm/inm-7/research-groups/applied-machine-learning-aml) group at [Forschungszentrum Juelich](https://www.fz-juelich.de/en), Germany.
## Installation
Use `pip` to install from PyPI like so:
```
pip install julearn
```
You can also install via `conda`, like so:
```
conda install -c conda-forge julearn
```
## Licensing
julearn is released under the AGPL v3 license:
julearn, FZJuelich AML machine learning library.
Copyright (C) 2020, authors of julearn.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
## Citing
If you use julearn in a scientific publication, please use the following reference
> Hamdan, Sami, Shammi More, Leonard Sasse, Vera Komeyer, Kaustubh R. Patil, and Federico Raimondo. ‘Julearn: An Easy-to-Use Library for Leakage-Free Evaluation and Inspection of ML Models’. arXiv, 19 October 2023. https://doi.org/10.48550/arXiv.2310.12568.
Since julearn is also heavily reliant on scikit-learn, please also cite them: https://scikit-learn.org/stable/about.html#citing-scikit-learn
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