The use of a loss function in machine learning is solely to determine the accuracy of predictions without considering the cost of errors.

The statement is incorrect. A loss function measures how far a prediction is from the true value and is used to guide the training of a model, not just to evaluate accuracy. By assigning different penalties to different kinds of errors, the loss function can encode the cost of mistakes—for example, giving a larger penalty for misclassifying a malignant tumor than a benign one. During training, the algorithm adjusts its parameters to minimize this loss, effectively learning which mistakes are most expensive. So, loss functions are central to both evaluating performance and steering learning toward the most important errors.