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While reading about model explainability and model accountability, the term surrogate model keeps appearing. I had an idea about what it is but it does not seem to make sense anymore:

  • What is a surrogate models?

  • Why are surrogate models good for explainability?

Carlos Mougan
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2 Answers2

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A surrogate model is an approximation model for a given function.

The original function is generally a black box function that we can sample from and based on the samples we can optimize our surrogate model to approximate the behaviour of the original function.

A surrogate model can be a neural network, an ensemble method, a gaussian process which can be interpreted and used later for explainability.

mirimo
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A "surrogate" is just a stand-in or proxy. In data science the word "surrogate" is used in more than one way (Bayesian hyperparameter optimization comes to mind). For interpretability, it seems to be used mostly to mean a more-interpretable model (maybe linear/logistic regression) that is trained to approximate the main, usually black-box, model (maybe boosted trees or a neural network).

See e.g. https://christophm.github.io/interpretable-ml-book/global.html. (The author refers to LIME as a "local surrogate" method and differentiates the "global surrogate" method, but I'm used to hearing just "surrogate" as a global one.)

Ben Reiniger
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  • I had exactly the same example in my mind, the GP surrogate model to approximate the cross validation score as a function of the hyperparameters, a great example to explain the surrogate model. – mirimo Jul 16 '20 at 08:35
  • thanks Ben! Its a great explanation – Carlos Mougan Jul 16 '20 at 10:44