MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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A game theoretic approach to explain the output of any machine learning model.
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Latest capture 2026-07-16 03:05
7 captures since 2026-05-22
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Scanned 2026-07-16 03:05
CMakeLists.txt
c-cpp ecosystem,
3 dependencies
pyproject.toml
python ecosystem,
56 dependencies
javascript/package.json
javascript ecosystem,
17 dependencies
javascript/package-lock.json
javascript ecosystem,
778 dependencies
docs/user_studies/sickness_scores/package.json
javascript ecosystem,
12 dependencies
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🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Model interpretability and understanding for PyTorch
Fit interpretable models. Explain blackbox machine learning.
Algorithms for explaining machine learning models
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑🔬