As introduced in Chapter 4, setting up a learning problem requires the selection of an inductive bias, which consists of a model class ℋ and a training algorithm. By the no-free-lunch theorem, this first step is essential in order to make generalization possible. A trained model generalizes if it performs well outside the training set, on average with respect to the unknown population distribution.
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