Which activation function is used to turn logits into probabilities for multiclass classification?

In multiclass classification tasks, the softmax activation function is employed to convert logits, which are raw prediction scores from the model, into probabilities that sum to one across all classes. The softmax function works by exponentiating each logit and then normalizing these exponentials by dividing them by the sum of all exponentials. This ensures that each output value is between 0 and 1, representing the probability of each class, and all probabilities add up to 1.

Using softmax allows models to predict the most probable class among multiple classes by interpreting the output probabilities, facilitating better decision-making based on the model’s confidence in each class. Other activation functions, while useful in various contexts, do not serve this specific purpose effectively—sigmoid is limited to binary classification, hyperbolic tangent can output negative values, and ReLU does not provide a way to interpret outputs as probabilities across multiple classes.