MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
This project helps data scientists and machine learning engineers understand why their predictive models make certain decisions. It takes a trained machine learning model and its input data, then generates easy-to-understand visualizations and reports that explain the model's behavior. The output helps both technical and non-technical stakeholders gain trust and insights into the model's predictions.
3,150 stars. Used by 1 other package. Actively maintained with 3 commits in the last 30 days. Available on PyPI.
Use this if you need to clearly explain the reasoning behind your machine learning model's predictions to both technical and non-technical audiences.
Not ideal if you are looking for a tool to build or train machine learning models, as this focuses solely on interpreting existing models.
Stars
3,150
Forks
373
Language
Jupyter Notebook
License
Apache-2.0
Last pushed
Feb 06, 2026
Commits (30d)
3
Dependencies
19
Reverse dependents
1
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