MAIF / shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Display clear and understandable results. plots and outputs use explicit labels for each feature and its values
- Allow Data Scientists to quickly understand their models using a webapp to easily navigate between global and local explainability, and understand how the different features contribute: Live Demo Shapash-Monitor
- Summarize and export local explanation
- Evaluate the quality of your explainability with various metrics
- Effortlessly share and discuss results with non-Data users
Get started
pip install shapash- Maintenance
- Active
- Last commit 30 days ago
- Stars
- 3,271
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v2.10.0
- Released 30 days ago
- Built with
- Jupyter Notebook
- 87% of the code
Is it looked after?
- Contributors
- 8
- Last 90 days
- Issues
- 89% closed
- 32 still open
- Pull requests
- 454 merged
- 5 waiting
- Releases
- 51
- 4 in the last year
- Contributing guide (opens in new tab)
- No security policy
- No code of conduct
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| shap / shap | 25,857 | 2 days ago | MIT | No downloads |
| explainX / explainx | 455 | 4 months ago | MIT | No downloads |
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More: languages, topics and project record
Languages
- Jupyter Notebook 87%
- Python 13%
- CSS <1%
- JavaScript <1%
Topics
- python
- machine-learning
- explainability
- explainable-ml
- transparency
- ethical-artificial-intelligence
- shap
- lime
- interpretability
Project record
- JobOther AI tools
- StatusListed
- Commits2,189
- Forks391
- Watching31
- Stars in 28 days+7
- CreatedApr 29, 2020
- First seen by usNov 7, 2026
- CheckedNov 12, 2026