Surrogate Linear Explanation Model

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Apr 17, 2023

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Apr 17, 2023 08:17 AM

A global surrogate model is an interpretable model that is trained to approximate the predictions of a black box model. We can draw conclusions about the black box model by interpreting the surrogate model. Solving machine learning interpretability by using more machine learning.

A surrogate linear explanation model is a type of machine learning model that is trained to approximate the behavior of a more complex model, such as a neural network or a decision tree, in a simpler and more interpretable way.

The goal of a surrogate linear explanation model is to provide a more transparent explanation of the original model's decision-making process, which can help users better understand how the model works and what factors are most important in driving its predictions.

The surrogate model typically uses linear regression, logistic regression, or another type of linear model to approximate the behavior of the original model, using the same input features and output predictions. The resulting model is easier to interpret and may be used to identify which features are most important in predicting the model's output. However, it may not capture all the nuances of the original model and may have reduced accuracy compared to the original model.