The curse of dimensionality
The curse of dimensionality refers to a problem in statistical modelling and machine learning. A model with more inputs could theoretically produce a more accurate prediction. The problem is that as the number of inputs increases, it becomes harder to train the model because it becomes nearly impossible to find enough practice data to model every possible combination of inputs. This is the curse of dimensionality.
Also worth noting that this isn't just a problem in machine learning. It is a phenomenon of mathematical modelling in general.
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