The softmax function is a mathematical tool used in machine learning, particularly in classification tasks. It converts a vector of raw scores, known as logits, into probabilities that sum to one. This allows the model to interpret the output as the likelihood of each class being the correct one. In practice, softmax is often applied in the final layer of a neural network. By applying this function, the model can provide a clear prediction for multi-class problems, making it easier to understand which class is most likely based on the input data.