Comprehensive and Detailed In-Depth Explanation=
Loss is a metric that quantifies the difference between a model's predictions and the actual target values, indicating how incorrect (or "wrong") the predictions are. Lower loss means better performance, making Option B correct. Option A is false-loss isn't about prediction count. Option C is incorrect-loss decreases as the model improves, not increases. Option D is wrong-loss measures overall error, not just correct predictions. Loss guides training optimization.
OCI 2025 Generative AI documentation likely defines loss under model training and evaluation metrics.