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2022 JMLR JMLR 2022

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2022. (edit, beta)

🌉 Interdisciplinary Bridge — Computer Science and Machine Learning
🧭 Keyword Pioneer — data integrity
🐣 Hot Topic Early Bird — data quality
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Application Areas > Data Augmentation Machine Learning > Application Areas > Domain Adaptation Computer Science > Applications > Software Engineering Machine Learning > Learning Types > Evaluation

Keywords

distribution shift prediction error data quality model validation model testing data integrity machine learning testing
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