Softmax is a mathematical function that converts a set of raw model scores into probabilities that add up to 100%, commonly used at the output of a classification model.
What it means in plain English
When a classification model produces raw scores for each possible category, softmax turns them into a clean probability distribution — each category gets a probability, and together they sum to one. This makes the output interpretable: instead of arbitrary numbers, you get “70% cat, 25% dog, 5% fox.” The highest probability is the model’s prediction, and the values show its confidence.
It is a standard final step in many neural networks that classify things.
A simple example
An image model’s raw scores might be meaningless on their own. Softmax converts them into “82% dog, 15% wolf, 3% fox,” so you can see both the prediction and how confident the model is in it.
Why it matters
Softmax is how classification models express their answers as understandable probabilities. It turns raw internal numbers into a confidence distribution across categories, which is essential for interpreting and using a model’s predictions.
Related terms
- Activation Function — the family softmax belongs to.
- Classification — where softmax is typically used.
- Neural Network — where softmax is applied at the output.
Frequently asked questions
What does softmax do?
Softmax converts a set of raw model scores into probabilities that sum to one, so the output can be read as the model’s confidence across possible classes.
Where is softmax used?
Typically at the output layer of classification models, and inside attention mechanisms, to turn scores into a probability distribution.
