Which concept refers to the loss calculated by comparing the model's probability distribution with the one-hot encoding?

Cross-Entropy Loss is a critical concept in machine learning, particularly in classification tasks involving neural networks. It quantifies the difference between two probability distributions: the predicted probability distribution generated by the model and the actual distribution represented by the one-hot encoding of the true labels.

In a one-hot encoded format, each class label is transformed into a vector where the index corresponding to the correct class is set to 1 (indicating high probability) and all other indices are set to 0 (indicating low probability). The cross-entropy loss measures how well the predicted probabilities align with these true distributions. A lower value of cross-entropy indicates a better performance of the model, as it suggests that the predicted probabilities are closer to the actual labels. This loss function is widely used in training classification models because it effectively penalizes incorrect predictions based on the confidence of the model.

Understanding cross-entropy loss is crucial for optimizing a model during training since it directly impacts the adjustments made to the model's parameters based on the error in its predictions compared to the expected results, represented by the one-hot encoding.