What is a characteristic of softmax as an activation function?

The characteristic of softmax as an activation function is that it outputs class probabilities that sum to one. This function is commonly used in the final layer of classification models, especially when dealing with multi-class problems.

Softmax takes a vector of raw scores (logits) and transforms these scores into a probability distribution across multiple classes. Each output probability is proportional to the exponent of the input score, making it easier to interpret the resulting values as probabilities. The total sum of all the probabilities produced by softmax equals one, which is essential in tasks where you want to determine the likelihood of each class being the correct one.

Using softmax in classification problems allows the model to provide clear probabilistic output, enabling the selection of the class with the highest probability as the model's prediction. This feature differentiates it from binary decision-making functions or non-probabilistic outputs that do not provide such a distribution over multiple classes.