Cross-Entropy Loss

Cross-entropy loss evaluates how closely a model’s predicted probability distribution matches the correct output. Predictions that assign high probability to the correct class receive a lower loss, while confident incorrect predictions receive a higher loss. During training, optimization algorithms adjust the model’s parameters to minimize this loss and improve its predictions.

Cross-entropy loss provides a clear signal for training classification models. By heavily penalizing confident but incorrect predictions, it helps models learn more accurate probability distributions and supports effective optimization during neural network training.