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Interpreting Machine Learning Models - Research Book

Interpreting Machine Learning Models - Research Book

Hot Research Book in Interpreting Machine Learning Models

Author(s) Name:  Anirban Nandi, Aditya Kumar Pal

About the Book:

   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 will begin by reviewing the theoretical aspects of machine learning interpretability. In the first few sections you will 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 will dive deep into the technical details of the interpretability domain. Starting off with the general frameworks of different types of methods, you will 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 book concludes by showing how data effects interpretability and some of the pitfalls prevalent when using explainability methods.

Table of Contents

  • The Evolution of Machine Learning
  • Introduction to Model Interpretability
  • Machine Learning Interpretability Taxonomy
  • Common Properties of Explanations Generated by Interpretability Methods
  • Human Factors in Model Interpretability
  • Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches
  • Interpretable ML and Explainable ML Differences
  • The Framework of Model Explanations
  • Feature Importance Methods: Details and Usage Examples
  • Detailing Rule-Based Methods
  • Detailing Counterfactual Methods
  • Detailing Image Interpretability Methods
  • Explaining Text Classification Models
  • The Role of Data in Interpretability
  • The Eight Pitfalls of Explainability Methods

    • ISBN:  978-1-4842-7802-4

    Publisher:  Springer

    Year of Publication:  2022

    Book Link:  Home Page Url