Deepchecks: A Library for Testing and Validating Machine Learning Models and Data

Shir Chorev; Philip Tannor; Dan Ben Israel; Noam Bressler; Itay Gabbay; Nir Hutnik; Jonatan Liberman; Matan Perlmutter; Yurii Romanyshyn; Lior Rokach

Deepchecks: A Library for Testing and Validating Machine Learning Models and Data

Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach

Department of Software and Information Systems Engineering

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

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 (AGPL) and relies on core libraries from the scientific Python ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy. Source code, documentation, examples, and an extensive user guide can be found at https://github.com/deepchecks/deepchecks and https://docs.deepchecks.com/.

Original language	English
Article number	285
Pages (from-to)	12990-12995
Journal	Journal of Machine Learning Research
Volume	23
Issue number	1
State	Published - 1 Jan 2022

Keywords

Bias
Concept Drift
Data Leakage
Explainable AI (XAI)
MLOps
Python
Supervised Learning
Testing Machine Learning

ASJC Scopus subject areas

Software
Control and Systems Engineering
Statistics and Probability
Artificial Intelligence

Cite this

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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 (AGPL) and relies on core libraries from the scientific Python ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy. Source code, documentation, examples, and an extensive user guide can be found at https://github.com/deepchecks/deepchecks and https://docs.deepchecks.com/.",

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Deepchecks: A Library for Testing and Validating Machine Learning Models and Data

Abstract

Keywords

ASJC Scopus subject areas

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