Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Arxiv
March 16, 2022
This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. The 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.
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