Surrogate Model (interpretability)

≡Description

A surrogate model is an approximation of a more complex model that is easier to analyze. In the context of machine learning, it is used to simplify the interpretation of models that would otherwise be difficult to understand due to their complexity. These surrogate models are generally simpler and can be linear or decision tree-based, allowing researchers and professionals to gain a clearer insight into how complex models function. The idea is that, although the surrogate model does not capture all the complexity of the original model, it can provide valuable information about the relationships between variables and the importance of each in the prediction. This is especially relevant in areas where interpretability is crucial, such as in data science, medicine, or finance, where model-based decisions must be understandable and justifiable. Furthermore, surrogate models can be used to validate and verify the behavior of more complex models, ensuring that there are no biases or unexpected behaviors in the predictions made by the original model.