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In data-driven modeling we have data that describes some system of interest, but we don't know many (or any) properties of a model that can describe it. Instead, we use mathematically flexible models that are useful in fitting and making predictions. These include models like interpolation, neural networks, Gaussian Processes, Random forests ...
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