Surrogate Model
A surrogate model is a simplified model that is used in place of a more complex model, with the goal of reducing the computational cost of optimization or simulation tasks. In data science and artificial intelligence, surrogate models are often used in the context of machine learning, where they can be used to approximate the behavior of a more complex model, such as a neural network or a deep learning model. Surrogate models can be trained on a subset of the data used to train the more complex model, and can be used to make predictions or generate new data points. Surrogate models can be particularly useful in situations where the more complex model is computationally expensive to train or evaluate, or where the data set is too large to be processed efficiently by the more complex model.
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