Books  |    |  February 21, 2019

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

Book by Christoph Molnar.
Self published and available for free online.
318 pages.

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. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.

All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Table of Contents

  • Preface
  • 1 Introduction
    • 1.1 Story Time
    • 1.2 What Is Machine Learning?
    • 1.3 Terminology
  • 2 Interpretability
    • 2.1 Importance of Interpretability
    • 2.2 Taxonomy of Interpretability Methods
    • 2.3 Scope of Interpretability
    • 2.4 Evaluation of Interpretability
    • 2.5 Properties of Explanations
    • 2.6 Human-friendly Explanations
  • 3 Datasets
    • 3.1 Bike Rentals (Regression)
    • 3.2 YouTube Spam Comments (Text Classification)
    • 3.3 Risk Factors for Cervical Cancer (Classification)
  • 4 Interpretable Models
    • 4.1 Linear Regression
    • 4.2 Logistic Regression
    • 4.3 GLM, GAM and more
    • 4.4 Decision Tree
    • 4.5 Decision Rules
    • 4.6 RuleFit
    • 4.7 Other Interpretable Models
  • 5 Model-Agnostic Methods
    • 5.1 Partial Dependence Plot (PDP)
    • 5.2 Individual Conditional Expectation (ICE)
    • 5.3 Accumulated Local Effects (ALE) Plot
    • 5.4 Feature Interaction
    • 5.5 Permutation Feature Importance
    • 5.6 Global Surrogate
    • 5.7 Local Surrogate (LIME)
    • 5.8 Scoped Rules (Anchors)
    • 5.9 Shapley Values
    • 5.10 SHAP (SHapley Additive exPlanations)
  • 6 Example-Based Explanations
    • 6.1 Counterfactual Explanations
    • 6.2 Adversarial Examples
    • 6.3 Prototypes and Criticisms
    • 6.4 Influential Instances
  • 7 Neural Network Interpretation
    • 7.1 Learned Features
  • 8 A Look into the Crystal Ball
    • 8.1 The Future of Machine Learning
    • 8.2 The Future of Interpretability

About the Author

Christoph Molnar is a data scientist and PhD candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. He is passionate about using statistics and machine learning on data to make humans and machines smarter.


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