nitin2468git/ml-explainability-toolkit
ML model interpretability with SHAP, LIME, and Partial Dependence Plots
14
/ 100
Experimental
No License
No Package
No Dependents
Maintenance
13 / 25
Adoption
0 / 25
Maturity
1 / 25
Community
0 / 25
Stars
—
Forks
—
Language
Jupyter Notebook
License
—
Last pushed
Mar 16, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/nitin2468git/ml-explainability-toolkit"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
obss/sahi
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
81
MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent...
79
understandable-machine-intelligence-lab/Quantus
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
72
SeldonIO/alibi
Algorithms for explaining machine learning models
72
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
72