NLLLoss measures negative log likelihood during a PyTorch optimization loop. Its log-based score can be converted back into a likelihood, making model confidence easier to inspect and explain. section 8.2.3
Turning log loss into likelihood
For the name “Khalid,” a model reports log losses of −1.17, −1.35, and −1.80 for different nationality predictions. Applying the exponential gives likelihoods of 0.31, 0.26, and 0.17, respectively.
Because the predictions are probabilities, inspecting NLLLoss-related outputs helps communicate how confident a model is to teammates, managers, or users. Treat those confidence values as evidence to examine, not as guarantees. section 8.2.3
