Chapter 4 Interpretable Models Chapter 4 Interpretable Models The easiest way to achieve interpretability is to use only a subset of algorithms that create interpretable models...

4.7 Other Interpretable Models 4.7.1 Naive Bayes Classifier 4.7.2 K-Nearest Neighbors 4.7 Other Interpretable Models The list of interpretable models is constantly growing an...

5.6 Global Surrogate 5.6.1 Theory 5.6.2 Example 5.6.3 Advantages 5.6.4 Disadvantages 5.6.5 Software 5.6 Global Surrogate A global surrogate model is an interpretable model...

2.2 Taxonomy of Interpretability Methods 2.2 Taxonomy of Interpretability Methods Methods for machine learning interpretability can be classified according to various criteria....

Interpretable Machine Learning A Guide for Making Black Box Models Explainable. Preface Interpretable Machine Learning A Guide for Making Black Box Models Explainable. Chri...

Chapter 10 Citing this Book Chapter 10 Citing this Book If you found this book useful for your blog post, research article or product, I would be grateful if you would cite thi...

2.6 Human-friendly Explanations 2.6.1 What Is an Explanation? 2.6.2 What Is a Good Explanation? 2.6 Human-friendly Explanations Let us dig deeper and discover what we humans ...

Chapter 1 Introduction Chapter 1 Introduction This book explains to you how to make (supervised) machine learning models interpretable. The chapters contain some mathematical f...

5.7 Local Surrogate (LIME) 5.7.1 LIME for Tabular Data 5.7.1.1 Example 5.7.2 LIME for Text 5.7.2.1 Example 5.7.3 LIME for Images 5.7.3.1 Example 5.7.4 Advantages 5.7.5 Disad...

Chapter 2 Interpretability Chapter 2 Interpretability There is no mathematical definition of interpretability. A (non-mathematical) definition I like by Miller (2017)3 is: Int...