AmirhosseinHonardoust/Shap-Mini

A minimal, reproducible explainable-AI demo using SHAP values on tabular data. Trains RandomForest or LogisticRegression models, computes global and local feature importances, and visualizes results through summary and dependence plots, all in under 100 lines of Python.

26
/ 100
Experimental
No Package No Dependents
Maintenance 6 / 25
Adoption 6 / 25
Maturity 9 / 25
Community 5 / 25

How are scores calculated?

Stars

20

Forks

1

Language

Python

License

MIT

Last pushed

Nov 09, 2025

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AmirhosseinHonardoust/Shap-Mini"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.