PAIR-code/lit

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

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

Interpretability and explainability of data and machine learning models

Lime: Explaining the predictions of any machine learning classifier

Fit interpretable models. Explain blackbox machine learning.

Model interpretability and understanding for PyTorch

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

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