What is multiclass classification?

Multiclass classification is a supervised machine learning task in which a model assigns an observation to exactly one of three or more mutually exclusive classes.

Simple example

A customer-support system may classify each new ticket as one of:

• Billing
• Technical Support
• Account Access

Each ticket receives one predicted category, even though the model considers all three possibilities.

How does it work?

• Training: learn patterns from examples labeled with one of the available classes.
• Prediction: compare the model's scores or estimated probabilities for the classes and select a class, often the one with the highest value.

For example, a model might output:

• Billing: 0.15
• Technical Support: 0.75
• Account Access: 0.10

The predicted class is Technical Support. These numbers illustrate an example of a model that outputs class probabilities; not every classifier provides calibrated probabilities.

How is it different from other classification tasks?

• Binary classification: exactly two possible classes, such as Spam and Not Spam.
• Multiclass classification: three or more possible classes, but only one is assigned per observation.
• Multilabel classification: an observation can receive several labels at once.

How is it evaluated?

Common measures include accuracy, a confusion matrix, and precision, recall, or F1-score computed per class. Macro-averaged scores help reveal poor performance on smaller classes that overall accuracy may hide.

In short, multiclass classification predicts one category from three or more mutually exclusive options, such as routing a support ticket to exactly one department.