In the context of machine learning, how does cross-entropy loss serve as an effective loss function for model evaluation metrics, particularly in classification tasks?

Cross‑entropy loss measures how far a model’s predicted probabilities are from the true labels, so it directly rewards accurate probability estimates. In a classification task, the loss is low when the model assigns high probability to the correct class and high when it misclassifies, making it a natural fit for evaluating accuracy and calibration. The loss is differentiable, allowing gradient‑based training to adjust weights in the direction that improves predictions. For example, if a model predicts a 0. 8 probability for the true class but the true label is 1, the loss is small, but if it predicts 0.