A mathematical formula that measures how wrong the model's predictions are. The choice of loss function determines what the model optimizes for; a model trained to minimize one type of error may perform poorly by other measures. This is relevant when evaluating whether a model was designed appropriately for its intended use: a model optimizing for average accuracy may systematically fail on minority cases.
See: Gradient descent; Training