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

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 ResearchSummaryThis 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 InstituteWho the book is forThis 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'sYou'll learn aboutThe 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 conceptAbout the authorThe author, Christoph Molnar, is an expert in machine learning and statistics, with a Ph.D. in interpretable machine learning.OutlineAbout the Book1 Introduction2 Interpretability3 Goals of Interpretability4 Methods Overview5 Data and Models6 Interpretable ModelsLinear RegressionLogistic RegressionGLM, GAM and moreDecision TreeDecision RulesRuleFitOther Interpretable Models7 Local Model-Agnostic MethodsCeteris Paribus PlotsIndividual Conditional Expectation (ICE)LIMECounterfactual ExplanationsScoped Rules (Anchors)Shapley ValuesSHAP8 Global Model-Agnostic MethodsPartial Dependence Plot (PDP)Accumulated Local Effects (ALE) PlotFeature InteractionFunctional DecompositonPermutation Feature ImportanceLeave One FEature Out (LOFO) Importance)Surrogate ModelsPrototypes and Criticisms9 Neural Network InterpretationLearned FeaturesPixel Attribution (Saliency Maps)Detecting ConceptsAdversarial ExamplesInfluential Instances10 Beyond the MethodsEvaluation of Interpetability MethodsStory TimeThe Future of Interpretability Read more