What is multilabel classification?

Multilabel classification is a supervised machine learning task in which one observation can belong to multiple classes at the same time.

Unlike multiclass classification, which selects exactly one category from several options, multilabel classification predicts which labels apply to each observation. It may assign none, one, or several labels.

Simple example

Suppose a customer-support ticket says: *“My delivery was late, and I want a refund.”*

The system could assign both:

• Delivery Issue
• Refund Request

The labels are not mutually exclusive, so choosing only one would lose useful information.

How does multilabel classification work?

• Training data: each example has a set of applicable labels rather than one exclusive class.
• Prediction: the model produces a score or estimated probability for each label.
• Decision: apply thresholds to decide which labels to assign. Thresholds may differ by label and should be tuned using validation data.

For instance, a model might output:

• Delivery Issue: 0.92 → assign
• Refund Request: 0.81 → assign
• Technical Issue: 0.08 → do not assign

These decisions assume a threshold of 0.50; the best threshold depends on the task and the costs of mistakes.

Multiclass vs. multilabel

• Multiclass: a news article is categorized as exactly one of Sports, Business, or Politics.
• Multilabel: an article about sports sponsorship can receive both Sports and Business.

How do you evaluate it?

Useful metrics include precision, recall, and F1-score (micro- or macro-averaged). Subset accuracy is particularly strict: all predicted labels for an observation must exactly match its true label set.

In short, use multilabel classification when categories can overlap and an observation may need several labels simultaneously.