Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
This work offers a practical solution for machine learning practitioners by providing a library to test and validate models and data, though it is incremental as it builds on existing tools without introducing new methods.
The authors introduced Deepchecks, a Python library designed for comprehensive validation of machine learning models and data, addressing issues like predictive performance and data integrity to provide an easy-to-use tool for practitioners.
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 \url{https://github.com/deepchecks/deepchecks} and \url{https://docs.deepchecks.com/}.