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Chapter 8 Global Model-Agnostic Methods

Global methods describe the average behavior of a machine learning model. The counterpart to global methods are local methods. Global methods are often expressed as expected values based on the distribution of the data. For example, the partial dependence plot, a feature effect plot, is the expected prediction when all other features are marginalized out. Since global interpretation methods describe average behavior, they are particularly useful when the modeler wants to understand the general mechanisms in the data or debug a model.

In this book, you will learn about the following model-agnostic global interpretation techniques: