Also known as: Cross entropy
Cross-entropy is a loss function that measures the difference between a predicted probability distribution and a target distribution. It is widely used in classification and language modeling, including multi-class tasks.
A generalization of Log Loss to multi-class classification problems. Cross-entropy quantifies the difference between two probability distributions. See also perplexity.