- Shapash: Making ML Models Understandable by Everyone
(link)
- Explainable AI Cheat Sheet
(link)
- Explain Your Model with the SHAP Values
(link)
- SHAP Doc
(link)
- SHAP git
(link)
- Interpretable Machine Learning - Christoph Molnar - Book
(link)
- Guide to Interpretable Machine Learning
(link)
- Intuitive Interpretation of Random Forest
(link)
- Increasing Tree Classifier interpretability with SHAP
(link)
- Random Forest Interpretation
(link)
- treeinterpreter - Interpreting Tree-Based Model’s Prediction of Individual Sample
(link)
- Why is that house so expensive? Ask a Random Forest!
(link)
- Interpretable Machine Learning with Python
(link)
- ELI5 Doc
(link)
- Demystifying Model Interpretation using ELI5
(link)
- How to Use eli5 to Understand sklearn Models, their Performance, and their Predictions
(link)