Model interpretability
Model interpretability — Making your model confesses: Surrogate models
For an introduction to the subject see:
As we saw in my Introduction to the Model interpretability, the most straightforward way to get an interpretable machine learning model is to use an algorithm that creates interpretable models at the very beginning. This includes models like Linear Models, Decision Trees, etc.
General Idea
Surrogate models try to extend this idea by training an interpretable model to “mimic” the behavior of a black-box model hoping that by understanding the “mimic” model we will get an understanding of how the black-box model behaves. Hence, surrogate models are a model-agnostic method since they do not require any information about how the black-box model works.
Local vs Global
This method is usually called “global” surrogate models since they try to approximate the behavior for the entire black-box model. On the other hand, there is a variation of this method called “local” surrogate models, where the interpretable model only tries to approximate the behavior of the black-box in a restricted area of the input space (i.e. the…
