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Fit interpretable models. Explain blackbox machine learning.
Evaluation and Tracking for LLM Experiments and AI Agents
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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.
Interpretability and explainability of data and machine learning models
moDel Agnostic Language for Exploration and eXplanation
XAI - An eXplainability toolbox for machine learning
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).