Which misconception about Evaluation metrics is avoided by the correct answer? Consider practical model design.

A. Loss is never computed in deep learning B. Evaluation metrics such as accuracy, precision, recall, and loss help compare models C. Evaluation metrics are irrelevant after training D. Accuracy is always sufficient for imbalanced data