Skip to content
conftrace_
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 - Artificial Intelligence and Computer Science
🐣 Hot Topic Early Bird - data quality

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
Download PDF

More from JMLR 2022

All JMLR 2022 papers →
On the Convergence Rates of Policy Gradient Methods
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation
Integral Autoencoder Network for Discretization-Invariant Learning
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models