Interpreting Machine Learning Models - Learn Model Interpretability and Explainability Methods

• Main Author: Anirban Nandi, Aditya Kumar Pal
• Format: eBook
• Language: English
• Published: Berkeley, CA Apress, an imprint of Springer Nature 2022; Apress; Apress L. P
• Edition: 1
• Subjects: Computer Science; Data mining; General References; Machine Learning; Professional and Applied Computing; Python; Python (Computer program language); Software Engineering
• ISBN: 9781484278017; 1484278011; 9781484278024; 148427802X
• DOI: 10.1007/978-1-4842-7802-4

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. You'll begin by reviewing the theoretical aspects of machine learning interpretability. In the first few sections you'll learn what interpretability is, what the common properties of interpretability methods are, the general taxonomy for classifying methods into different sections, and how the methods should be assessed in terms of human factors and technical requirements. Using a holistic approach featuring detailed examples, this book also includes quotes from actual business leaders and technical experts to showcase how real life users perceive interpretability and its related methods, goals, stages, and properties. Progressing through the book, you'll dive deep into the technical details of the interpretability domain. Starting off with the general frameworks of different types of methods, you'll use a data set to see how each method generates output with actual code and implementations.