tidymodels/lime

Lime: Explaining the predictions of any machine learning classifier

Explaining the output of machine learning models with more accurately estimated Shapley values

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

Fit interpretable models. Explain blackbox machine learning.

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

Interpretability and explainability of data and machine learning models

Shapley Interactions and Shapley Values for Machine Learning

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

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.