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Two faces of overfitting

Overfitting is on of the primary problems, if not THE primary problem in machine learning. There are many aspects to it, but in a general sense, overfitting means that estimates of performance on unseen test examples are overly optimistic. That is, a model generalizes worse then expected.

We explain two common cases of overfitting: including information from a test set in training, and the more insidious form: overusing a validation set.

 

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