Data Collection and Labeling
Supervised models learn from examples paired with answers. Collecting the raw examples and attaching correct answers, called labeling, is often the most expensive part of a project.
Collection sources
- Logs from an existing product capture real user behavior cheaply.
- Public datasets give a head start but may not match your domain.
- Manual gathering or sensors produce fresh data when nothing exists yet.
Labeling quality
Labels can be noisy when annotators disagree or guidelines are vague. Common safeguards include:
- Writing a clear labeling guide with examples of hard cases.
- Having multiple annotators label the same item and measuring agreement.
- Reserving expert review for ambiguous or high stakes items.
Cost and bias
Labeling at scale tempts teams to cut corners, but cheap labels can encode bias or systematic errors that the model then amplifies. A smaller, carefully labeled set often beats a large noisy one. Tracking who labeled what and when helps you audit problems later.
Key idea
Trustworthy labels come from clear guidelines and measured agreement, not from collecting the most data possible.