What is a label in machine learning?

A label in machine learning is the known output or category attached to a training example that a supervised model learns to predict.

Labels provide the correct answer for the examples used during training.

Simple example

Suppose we are training a model to detect spam emails.

Each email might have a label such as:

• Spam
• Not spam

The model receives the email as input and learns to predict the correct label.

Email features → Label

Labels in classification

The term label is used most often in classification problems, where the output belongs to a category.

Examples include:

• cat, dog, or bird for image classification
• fraud or not fraud for transaction detection
• approved or rejected for an application

Label vs target variable

• A target variable is the general output a supervised model tries to predict.
• A label usually refers to a known categorical target assigned to an example.

In practice, the terms are sometimes used interchangeably, especially when discussing supervised learning.

Labeled data

A dataset is called labeled when its training examples include these known outputs.

For example:

Image → cat
Image → dog
Image → cat

Here, cat and dog are the labels.

In short, a label is the known answer attached to a training example that tells a supervised model what output it should learn to predict.