test loss

A metric representing a model's loss against the test set. When building a model, you typically try to minimize test loss. That's because a low test loss is a stronger quality signal than a low training loss or low validation loss. A large gap between test loss and training loss or validation loss sometimes suggests that you need to increase the regularization rate.

Practitioners refer to test loss when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.