Price: $50.00
(as of Sep 11, 2024 09:03:07 UTC – Details)
Interpretable Machine Learning is a comprehensive guide to making machine learning models interpretable
“Pretty convinced this is the best book out there on the subject”
– Brian Lewis, Data Scientist at Cornerstone Research
Summary
This book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance. It also includes interpretation methods specific to deep neural networks, and discusses why interpretability is important in machine learning. 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?
“What I love about this book is that it starts with the big picture instead of diving immediately into the nitty gritty of the methods (although all of that is there, too).”
– Andrea Farnham, Researcher at Swiss Tropical and Public Health Institute
Who the book is for
This book is essential for machine learning practitioners, data scientists, statisticians, and anyone interested in making their machine learning models interpretable. It will help readers select and apply the appropriate interpretation method for their specific project.
“This one has been a life saver for me to interpret models. ALE plots are just too good!”
– Sai Teja Pasul, Data Scientist at Kohl’s
You’ll learn about
The concepts of machine leaning interpretabilityInherently interpretable modelsMethods to make any machine model interpretable, such as SHAP, LIME and permutation feature importanceInterpretation methods specific to deep neural networksWhy interpretability is important and what’s behind this concept
About the author
The author, Christoph Molnar, is an expert in machine learning and statistics, with a Ph.D. in interpretable machine learning.
Outline
Summary1 Preface by the Author2 Introduction3 Interpretability4 Datasets5 Interpretable Models5.1 Linear Regression5.2 Logistic Regression5.3 GLM, GAM and more5.4 Decision Tree5.5 Decision Rules5.6 RuleFit5.7 Other Interpretable Models6 Model-Agnostic Methods7 Example-Based Explanations8 Global Model-Agnostic Methods8.1 Partial Dependence Plot (PDP)8.2 Accumulated Local Effects (ALE) Plot8.3.1 Feature Interaction8.4 Functional Decompositon8.5 Permutation Feature Importance8.6 Global Surrogate8.7 Prototypes and Criticisms9 Local Model-Agnostic Methods9.1 Individual Conditional Expectation (ICE)9.2 Local Surrogate (LIME)9.3 Counterfactual Explanations9.4 Scoped Rules (Anchors)9.5 Shapley Values9.6 SHAP (SHapley Additive exPlanations)10 Neural Network Interpretation10.1 Learned Features10.2 Pixel Attribution (Saliency Maps)10.3 Detecting Concepts10.4 Adversarial Examples10.5 Influential Instances11 A Look into the Crystal Ball
ASIN : B09TMWHVB4
Publisher : Independently published (February 28, 2022)
Language : English
Paperback : 328 pages
ISBN-13 : 979-8411463330
Item Weight : 1.44 pounds
Dimensions : 7.44 x 0.77 x 9.69 inches
