What is Data Labeling?
Data labeling (or data annotation) is the process of attaching descriptive categories, tags, or expected outputs to raw examples in a dataset so that a machine learning model can learn from them during training.
How does it work?
If you want an AI to recognize stop signs, you cannot just show it thousands of random street photos. Someone—usually a human worker—must draw a box around the stop sign in every photo and explicitly label it "stop sign." Labels can be:
- Human-created: Manual annotation by individuals.
- Programmatically generated: Automated tagging based on simple rules.
- Model-assisted: An AI makes a first pass, and a human reviews it.
- Noisy: Labels that are accidentally incorrect or subjective.
What is a simple example?
In a customer support setting, a human might read a dataset of past emails and label them as "Refund Request," "Technical Issue," or "Spam." The AI then uses these labeled examples to learn how to categorize new emails automatically.
Why does it matter?
Data labeling is the foundation of supervised learning. The accuracy of the AI is entirely dependent on the quality of the labels. Poor, biased, or inconsistent data labeling directly results in flawed and unreliable AI models.