# Deepchecks: A Library for Testing and Validating Machine Learning Models and Data

arXiv: https://arxiv.org/abs/2203.08491 · HTML: https://mlsanity.com/paper/2203.08491/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [Do We Train on Test Data? Purging CIFAR of Near-Duplicates](https://mlsanity.com/paper/1902.00423/) (2019)

## Built on by (most cited first, 7 of 7)

- [Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles](https://mlsanity.com/paper/2302.08292/) (2023)
- [LUNA: A Model-Based Universal Analysis Framework for Large Language Models](https://mlsanity.com/paper/2310.14211/) (2023)
- [MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities](https://mlsanity.com/paper/2303.01998/) (2023)
- [How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation](https://mlsanity.com/paper/2404.05622/) (2024)
- [Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition](https://mlsanity.com/paper/2409.13409/) (2024)
- [Maturity Framework for Enhancing Machine Learning Quality](https://mlsanity.com/paper/2502.15758/) (2025)
- [DeepFix: Debugging and Fixing Machine Learning Workflow using Agentic AI](https://mlsanity.com/paper/2603.14099/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/deepchecks/deepchecks and urlhttps://docs.deepchecks.com/.