what is Multi-class classification ?

Multi-class classification is a classification task where a model must choose one class from three or more mutually exclusive classes.

Examples include:

• Classifying an image as cat, dog, or bird
• Recognizing a handwritten digit from 0 to 9
• Predicting one product category from several possible categories

How it works

A model usually produces one score for each class.

For example:

Cat   → 2.4
Dog   → 1.1
Bird  → 0.3

These raw scores can be converted into probabilities using the softmax function:

Cat   → 0.72
Dog   → 0.20
Bird  → 0.08

The class with the highest probability is typically selected as the prediction.

For how softmax performs this conversion, see “What is the softmax function?”.

Multi-class vs binary classification

In binary classification, the model chooses between exactly two classes.

In multi-class classification, the model chooses one class from three or more possibilities.

For the two-class case, see “What is Binary Classification ?”.

Important distinction

Multi-class classification assumes the classes are mutually exclusive for a given prediction. If multiple labels can be correct at the same time, that is a different problem called multi-label classification.

In short, multi-class classification assigns an input to one class from several possible classes, commonly using one output score per class and softmax to interpret those scores as probabilities.