(Ebook) Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods by Anirban Nandi, Aditya Kumar Pal ISBN 9781484278017, 1484278011

(Ebook) Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods by Anirban Nandi, Aditya Kumar Pal ISBN 9781484278017, 1484278011

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 the 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 codes and implementations. These methods are divided into different types based on their explanation frameworks with some common categories listed as feature importance based methods, rule based methods, saliency maps methods, counterfactuals, and concept attribution, etc. The book concludes by showing how data effects interpretability and how uncertainty, apart from common methods, can be used to interpret models. What You’ll Learn Understand machine learning model interpretability Explore the different properties and selection requirements of various interpretability methods Review the different types of interpretability methods used in the real life by technical experts Interpret the output of various methods and understand the underlying problems Who This Book Is For Machine learning practitioners, data scientists and statisticians interested in making machine learning models interpretable and explainable; academic students pursuing courses of data science and business analytics