Interpretable Machine Learning
Interpretable Machine Learning
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Interpretable Machine Learning

This book is 82% complete

Last updated on 2018-08-14

About the Book

"Thank you @ChristophMolnar for the great work on #MachineLearning Interpretability!" - @HJDLopes

"Book on interpretability of ML models, such an important topic often neglected" - @prdeepakbabu

"New book on interpretable #AI by @ChristophMolnar very much needed!" - @AjitJaokar

Machine learning has great potential for improving products, processes and research. But computers usually don’t explain their predictions, which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.

After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The later chapters focus on general model-agnostic tools for interpreting black box models and explaining individual predictions. In an ideal future, machines will be able to explain their decisions and the algorithmic age we move toward will be as human as possible.

The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. This book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning more interpretable.

A free HTML version of the book can be found at: https://christophm.github.io/interpretable-ml-book/

About the Author

Christoph Molnar
Christoph Molnar

On a mission to make algorithms more interpretable by combining machine learning and statistics.

About the Contributors

Yvonne
Yvonne

Cover designer

Table of Contents

  • Introduction
    • Storytime
    • What Is Machine Learning?
    • Definitions
  • Interpretability
    • The Importance of Interpretability
    • Criteria for Interpretability Methods
    • Scope of Interpretability
    • Evaluating Interpretability
    • Human-friendly Explanations
  • Datasets
    • Bike Sharing Counts (Regression)
    • YouTube Spam Comments (Text Classification)
    • Risk Factors for Cervical Cancer (Classification)
  • Interpretable Models
    • Linear Model
    • Logistic Regression
    • Decision Tree
    • Decision Rules (IF-THEN)
    • RuleFit
    • Other Interpretable Models
  • Model-Agnostic Methods
    • Partial Dependence Plot (PDP)
    • Individual Conditional Expectation (ICE)
    • Feature Interaction
    • Feature Importance
    • Global Surrogate Models
    • Local Surrogate Models (LIME)
    • Shapley Value Explanations
  • Example-based explanations
    • Counterfactual explanations
    • Adversarial Examples
    • Prototypes and Criticisms
    • Influential Instances
  • A Look into the Crystal Ball
    • The Future of Machine Learning
    • The Future of Interpretability
  • Contribute
  • Citation
  • Acknowledgements
  • Notes

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