
shapash
by MAIF (organization)
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models (description from the project’s GitHub page)
Repository facts
- Stars
- 3,264
- Forks
- 389
- Open issues and pull requests
- 40
- Primary language
- Jupyter Notebook
- License
- Apache License 2.0 (Apache-2.0)
- Owner
- MAIF · Organization
- Created
- Apr 29, 2020
- Last push
- Oct 16, 2026
Topics
Related projects
Projects that share this one’s GitHub topics.
Rank 1,165: interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
6,962 starsC++MITLast push Oct 18, 2026
Rank 7,876: jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
681 starsJupyter NotebookNo license file detectedLast push Jun 17, 2024
Rank 7,155: mmschlk/shapiq
Shapley Interactions and Shapley Values for Machine Learning
789 starsPythonMITLast push Oct 16, 2026
Rank 316: shap/shap
A game theoretic approach to explain the output of any machine learning model.
25,820 starsJupyter NotebookMITLast push Oct 18, 2026
Rank 3,897: microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. Thes…
1,847 starsTypeScriptMITLast push Oct 17, 2026
Rank 5,183: EthicalML/xai
XAI - An eXplainability toolbox for machine learning
1,269 starsPythonMITLast push Nov 29, 2025