TY  - BOOK
AU  -     Nandi, Anirban & Pal, Aditya Kumar
TI  -     Interpreting Machine Learning Models : Learn Model Interpretability and Explainability Methods
SN  - 9781484284094
U1  - 006.31 1
PY  - 2022///
PB  - Apress, 
KW  - Electronic books Machine learning Mathematical models
N1  - Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms; Chapter 1: The Evolution of Machine Learning
Chapter 2: Introduction to Model interpretability
Chapter 3: Machine Learning Interpretability Taxonomy
Chapter 4: Common Properties of Explanations Generated by Interpretability Methods
Chapter 5: Human Factors in Model Interpretability
Chapter 6: Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches
Chapter 7: Interpretable ML and Explainable ML Differences
Chapter 8: Framework of Model Explanations
Chapter 9: Feature Importance methods
Details and usage examples
Chapter 10: Detailing rule-based methods
Chapter 11: Detailing Counterfactual Methods
Chapter 12: Detailing Image interpretability methods
Chapter 13: Explaining text classification models
Chapter 14: Role of Data in Interpretability
Chapter 15: The 8 pitfalls of explainability methods
UR  - https://www.worldcat.org/title/1290840038
ER  -