A loss function assigns a numerical penalty to a prediction and its observed outcome. Its expected value is the population risk optimized by a statistical learning procedure.
The population risk of a prediction rule is its expected loss on a fresh observation from the target probability distribution.

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  1. Empirical risk minimization
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