Global explanations help to understand the model’s general behavior.
There are multiple forms of global explanations. For example, global explanations:
Can identify the important features that the model considers when making its predictions.
Highlight the relationship between different feature values and the model’s predictions.
Present the instances that are most influential towards the prediction of a given class and value.