Loss functions are mathematical tools used to quantify the difference between the predicted outcomes of a model and the actual outcomes observed in data. They serve as a critical component in decision-making processes by allowing practitioners to measure how well a model performs, guiding adjustments to improve predictions. The selection of an appropriate loss function can greatly influence optimal decision rules and is essential for understanding risk and expected utility in Bayesian statistics.
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