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Data Science
ML Workflows
Human-in-the-Loop
Incorporates human feedback into the ML pipeline to collect explicit labels.
Intent & Description
📋 Context
For supervised learning systems, collecting high-quality labels is essential. Human-in-the-loop patterns incorporate human judgment at key points in the pipeline.
Real-world Use Case
When ML systems require human feedback for training, validation, or active learning.
Source
Advantages
- Improves label quality
- Enables active learning
- Handles edge cases
Disadvantages
- Slower than automated labeling
- Human bottleneck
- Higher cost
Implementation Example
# Human-in-the-Loop
feedback = human_reviewer.label(predictions)
model.update_with_feedback(feedback)