The term „over-parameterised models“ originates from the fields of Artificial Intelligence, Industry and Industry 4.0, and Big Data and Smart Data. It refers to mathematical models, for example in AI, that have more parameters (i.e. adjustable values) than would actually be necessary to solve a particular problem.
Imagine you have a robot in a factory that needs to learn to sort different parts. If the model enabling the robot to learn has a very large number of parameters, this „over-parameterised model“ can often recognise far more patterns than are actually present. As a result, the robot might learn not only the real features but also random details that are not significant for the task at all.
Over-parameterised models are used because they offer high flexibility and often achieve better results on large datasets – provided there is enough data and the models are controlled correctly. The goal is to find a balance: the model should be complex enough to solve difficult tasks, but not so complex that it learns everything – even the unimportant.
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