Interpretable Machine Learning
A Guide for Making Black Box Models Explainable
Comprehensive guide to understanding and explaining black-box ML models for transparency.
About the book
Practical guide to interpretability methods (LIME, SHAP, partial dependence, etc.) for understanding ML models. Essential as models become more complex and deployment requires explainability and regulatory compliance.
Summary
Summary coming soon!