In what scenario would you use the softmax function?

The softmax function is specifically designed for multi-class classification tasks where the goal is to classify input data into one of several possible categories. It converts the raw output scores from a model (often referred to as logits) into a probability distribution over multiple classes. Each output is transformed such that they sum to one, allowing the interpretation of the outputs as probabilities.

In practical terms, when using softmax, you typically have a model that can output multiple scores for each class. The softmax function takes these scores and normalizes them, which helps to determine the most likely class by generating probabilities that indicate the likelihood of each class given the input. The class with the highest probability is often selected as the predicted class. This is particularly useful for tasks where there are more than two classes, making it inappropriate for binary classification, where other strategies like sigmoid functions are preferred.

In contrast, unsupervised learning does not typically involve class probabilities since it does not involve labeled inputs, while regression analysis focuses on predicting continuous values rather than categorical outcomes. Therefore, the application of the softmax function is best suited for multi-class classification scenarios, aligning perfectly with the correct choice.