Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Abstract
This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. Our goal is to provide an easy-to-use library comprising many checks related to various issues, such as model predictive performance, data integrity, data distribution mismatches, and more. The package is distributed under the GNU Affero General Public License and relies on core libraries from the scientific Python ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy.
Cite
Text
Chorev et al. "Deepchecks: A Library for Testing and Validating Machine Learning Models and Data." Machine Learning Open Source Software, 2022.Markdown
[Chorev et al. "Deepchecks: A Library for Testing and Validating Machine Learning Models and Data." Machine Learning Open Source Software, 2022.](https://mlanthology.org/mloss/2022/chorev2022jmlr-deepchecks/)BibTeX
@article{chorev2022jmlr-deepchecks,
title = {{Deepchecks: A Library for Testing and Validating Machine Learning Models and Data}},
author = {Chorev, Shir and Tannor, Philip and Israel, Dan Ben and Bressler, Noam and Gabbay, Itay and Hutnik, Nir and Liberman, Jonatan and Perlmutter, Matan and Romanyshyn, Yurii and Rokach, Lior},
journal = {Machine Learning Open Source Software},
year = {2022},
pages = {1-6},
volume = {23},
url = {https://mlanthology.org/mloss/2022/chorev2022jmlr-deepchecks/}
}