MDL, or Minimum Description Length, is a formalization of the principle of model selection in statistical learning. It operates on the idea that the best model for a given set of data is one that allows for the shortest possible encoding of both the model and the data. This approach effectively balances the complexity of the model against its fit to the data, promoting models that generalize well rather than merely fitting noise.
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