Cross-entropy loss is a measure used in machine learning to evaluate how well a model's predicted probabilities match the actual outcomes. It quantifies the difference between the true distribution of labels and the predicted distribution, with lower values indicating better performance. This loss function is particularly useful for classification tasks, where the goal is to assign input data to specific categories. In mathematical terms, cross-entropy loss calculates the negative log likelihood of the true labels given the predicted probabilities. It is commonly used in conjunction with softmax activation functions in neural networks, helping to optimize the model during training by adjusting its parameters to minimize the loss.