Data Labeling

Data Labeling

Definition

Data labeling is the process of assigning categories, tags, or attributes to raw data so machine learning models can learn from it. It is central to supervised learning.

Purpose

The purpose is to make raw datasets usable for training and evaluation. Labels provide the “answers” models need during learning.

Importance

  • Critical for building accurate supervised ML models.
  • Poor labeling reduces system reliability.
  • Often labor-intensive and costly.
  • Requires domain expertise in fields like medicine or law.

How It Works

  1. Define tasks and label schema.
  2. Segment raw data into units (images, sentences, audio clips).
  3. Assign labels manually or via semi-automated tools.
  4. Perform quality checks and inter-annotator agreement tests.
  5. Export labeled datasets for training.

Examples (Real World)

  • Shaip: labeling data for autonomous vehicles.
  • Kaggle datasets: labeled for ML competitions.
  • Radiology image datasets: labeled by medical experts.

References / Further Reading

  • Data Annotation for AI — NIST.
  • Annotating and Labeling Datasets — IEEE Transactions on Data Engineering.
  • ISO/IEC 24617: Semantic Annotation Framework — ISO.