When should I use cross-entropy loss?

When should I use cross-entropy loss?

Cross-entropy loss is typically used for classification problems, especially in neural networks.

Can you explain why it's preferred over other loss functions?

It's preferred because it measures the performance of a model whose output is a probability value between 0 and 1.

What types of problems is it best suited for?

It's best suited for multi-class classification problems, like image recognition or text classification.

Are there any situations where I shouldn't use it?

Yes, for regression tasks or when the output is not a probability distribution, other loss functions like MSE are better.