Author Description

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Part I Introduction -- 1 Introduction -- 1.1 The aim of the book -- 1.2 A bit of philosophy: three laws of model explanation -- 1.3 Terminology -- 1.4 Black-box models and glass-box models -- 1.5 Model-agnostic and model-specific approach -- 1.6 The structure of the book -- 1.7 What is included in this book and what is not -- 1.8 Acknowledgements -- 2 Model Development -- 2.1 Introduction -- 2.2 Model-development process -- 2.3 Notation -- 2.4 Data understanding -- 2.5 Model assembly (fitting) -- 2.6 Model audit -- 3 Do-it-yourself -- 3.1 Do-it-yourself with R -- 3.1.1 What to install? -- 3.1.2 How to work with DALEX? -- 3.1.3 How to work with archivist? -- 3.2 Do-it-yourself with Python -- 3.2.1 What to install? -- 3.2.2 How to work with dalex? -- 3.2.3 Code snippets for Python -- 4 Datasets and Models -- 4.1 Sinking of the RMS Titanic -- 4.1.1 Data exploration -- 4.2 Models for RMS Titanic, snippets for R -- 4.2.1 Logistic regression model -- 4.2.2 Random forest model -- 4.2.3 Gradient boosting model -- 4.2.4 Support vector machine model -- 4.2.5 Models' predictions -- 4.2.6 Models' explainers -- 4.2.7 List of model-objects -- 4.3 Models for RMS Titanic, snippets for Python -- 4.3.1 Logistic regression model -- 4.3.2 Random forest model -- 4.3.3 Gradient boosting model -- 4.3.4 Support vector machine model -- 4.3.5 Models' predictions -- 4.3.6 Models' explainers -- 4.4 Apartment prices -- 4.4.1 Data exploration -- 4.5 Models for apartment prices, snippets for R -- 4.5.1 Linear regression model -- 4.5.2 Random forest model -- 4.5.3 Support vector machine model -- 4.5.4 Models' predictions -- 4.5.5 Models' explainers -- 4.5.6 List of model-objects -- 4.6 Models for apartment prices, snippets for Python -- 4.6.1 Linear regression model.