Local Surrogate Models
Local surrogate models are interpretable models that are trained to approximate the predictions of a black-box model in a specific region of the input space. They are used to provide insights into the behavior of complex models and to explain their predictions. Local surrogate models can be trained using various techniques such as decision trees, linear models, or neural networks. The training data for these models is generated by perturbing the input data around a specific point of interest and recording the corresponding predictions of the black-box model. Once trained, local surrogate models can be used to analyze the importance of input features, to detect regions of the input space where the black-box model is most sensitive, and to identify potential biases or errors in the black-box model.
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