Interpreting Machine Learning Models
- Paperback: 366 pages
- Publisher: WOW! eBook (December 16, 2021)
- Language: English
- ISBN-10: 1484278011
- ISBN-13: 978-1484278017
Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods
Understand model interpretability methods and apply the most suitable one for your machine learning project. This Interpreting Machine Learning Models 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.
What You Will 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 real life by technical experts
- Interpret the output of various methods and understand the underlying problems
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. 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. The Interpreting Machine Learning Models book concludes by showing how data effects interpretability and some of the pitfalls prevalent when using explainability methods.
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