Surrogate Model in machine learning refers to an approximate model that is used in place of a more complex or expensive model to assist in understanding or optimization​.There are two common contexts for surrogate models:Interpretability: Here a surrogate model is a simpler, interpretable model (like a linear model or decision tree) trained to mimic the predictions of a complex model (like an ensemble or neural network) on a dataset. Because the surrogate is much easier to interpret, one can examine it to gain insights into the complex model’s decision logic. Essentially, the surrogate approximates the original model’s input-output mapping​33rdsquare.com, sacrificing some accuracy for transparency. For example, LIME (Local Interpretable Model-agnostic Explanations) fits local surrogate models around a prediction to explain individual decisions.Optimization/Simulation: In this scenario, a surrogate model approximates an expensive process or simulation. For instance, in engineering, instead of running a very slow physics simulation for every parameter set, one might train a faster regression model as a surrogate to predict outcomes. The surrogate is then used to explore the parameter space efficiently.In both cases, the surrogate model should be much easier or faster to evaluate than the original. It “stands in” for the original model or process. Because it’s an approximation, one must validate that it’s sufficiently accurate for the intended use. Surrogate models are powerful for reducing computational costs and for opening up black-box models to human understanding​.