Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

part I|62 pages


chapter 1|14 pages


chapter 2|12 pages

Model Development

chapter 3|6 pages


chapter 4|28 pages

Datasets and Models

part II|105 pages

Instance Level

chapter 6|18 pages

Break-down Plots for Additive Attributions

chapter 7|10 pages

Break-down Plots for Interactions

chapter 10|16 pages

Ceteris-paribus Profiles

chapter 11|8 pages

Ceteris-paribus Oscillations

chapter 12|10 pages

Local-diagnostics Plots

chapter 13|9 pages

Summary of Instance-level Exploration

part III|98 pages

Dataset Level

chapter 14|2 pages

Introduction to Dataset-level Exploration

chapter 15|21 pages

Model-performance Measures

chapter 16|14 pages

Variable-importance Measures

chapter 17|20 pages

Partial-dependence Profiles

chapter 19|15 pages

Residual-diagnostics Plots

chapter 20|4 pages

Summary of Dataset-level Exploration

part IV|30 pages


chapter 21|24 pages

Fifa 19

chapter 22|4 pages