Which loss function is commonly used for classification problems?

Cross entropy is a commonly used loss function for classification problems, particularly in scenarios involving multi-class classification. It measures the difference between two probability distributions: the predicted distribution and the true distribution (often represented as one-hot vectors).

In classification tasks, especially those handled by neural networks, the model outputs a probability distribution over the classes. The cross-entropy loss quantifies how well the predicted probabilities align with the actual class labels. A lower cross-entropy value indicates better model performance, as it reflects that the predicted probabilities are closer to the true probabilities.

Utilizing cross-entropy is particularly advantageous due to its effectiveness in optimizing probabilities in the context of softmax outputs, making it a standard choice for problems where the goal is to categorize data into distinct classes. This characteristic distinguishes it in scenarios like binary classification using logistic regression or multi-class classification using deep learning models.