A black box model refers to a system or process where the internal workings or details are unknown or not understood, and it's classified or evaluated only based on its inputs and outputs. The term is frequently used in a variety of fields like finance, computing, and machine learning. It originates from the notion of a 'black box', an object that you can't see inside, but you know what goes in and what comes out.
How Black Box Models work
In machine learning, black box models, like neural networks, make predictions based on a large amount of learned data. The decision-making process is complex and not easily explainable. Despite the lack of transparency, these models can be powerful tools as they have the potential to identify trends and relationships that might not be immediately evident.
In each of these cases, the focus is on the relationship between the inputs and outputs, not on the internal processes that lead to these outputs.
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