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

Research output: Working paper/PreprintPreprint

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 of many checks related to various types of 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 languageEnglish
Volumeabs/2203.08491
DOIs
StatePublished - 2022

Publication series

NameCoRR

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