NorskRegnesentral/shapr

A game theoretic approach to explain the output of any machine learning model.

For calculating global feature importance using Shapley values.

Shapley Interactions and Shapley Values for Machine Learning

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Lime: Explaining the predictions of any machine learning classifier

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

Fit interpretable models. Explain blackbox machine learning.

Interpretability and explainability of data and machine learning models

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).