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.1
A large gap between test loss and training loss or validation loss sometimes suggests that you need to increase the regularization rate.