Active Learning
A training strategy where the model identifies the most informative unlabeled examples and requests human labels only for those. This minimizes labeling effort by focusing on the examples that matter most.
Why It Matters
Active learning can reduce labeling costs by 50-90% by only asking humans to label the examples the model is most uncertain about.
Example
A medical imaging model identifying the 100 most ambiguous X-rays (out of 10,000 unlabeled) and asking a radiologist to label only those, maximizing learning per label.
Think of it like...
Like a student who asks the teacher questions only about the concepts they find confusing, rather than having the teacher explain everything from scratch.
Related Terms
Data Labeling
The process of assigning meaningful tags or annotations to raw data so it can be used for supervised learning. Labels tell the model what the correct answer should be for each training example.
Supervised Learning
A type of machine learning where the model is trained on labeled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict outputs for new, unseen inputs.
Human-in-the-Loop
A system design where humans are integrated into the AI workflow to provide oversight, make decisions, correct errors, or handle edge cases that the AI cannot reliably manage alone.
Annotation
The process of adding labels, tags, or metadata to raw data to make it suitable for supervised machine learning. Annotation can involve labeling images, transcribing audio, or tagging text.