When should I use softmax in neural networks?

When should I use softmax in neural networks?

You should use softmax in the output layer of a neural network when you want to classify inputs into multiple classes.

Why is softmax preferred for multi-class classification?

Softmax converts raw scores into probabilities, ensuring that the outputs sum to 1, which is ideal for multi-class scenarios.

Can softmax be used in binary classification?

Yes, but it's more common to use a sigmoid activation function for binary classification. Softmax can still be used if you treat it as a two-class problem.

What are the limitations of using softmax?

Softmax can be sensitive to outliers and may lead to overconfidence in predictions, especially with imbalanced classes.

Are there alternatives to softmax?

Yes, alternatives include sigmoid for binary classification and other functions like the logistic regression output.