What is Active Learning
A machine learning method in which the model itself selects the most useful examples for human labeling.
Definition
Active learning helps you save time and budget on data labeling. Instead of labeling thousands of random examples, the system finds the cases in which the model doubts the most. The expert marks them, and the model quickly improves the quality.
Example
For an image moderation system, a model can ask a person to mark controversial images where she is not sure whether this is acceptable content or a violation.
Why it matters
This is important for projects where markup is expensive: medicine, legal documents, industrial control, rare languages or complex moderation.
How it works
The model is trained on a small sample, evaluates unlabeled data, selects the most vague or informative examples, receives expert responses, and repeats the cycle.
Where it is used
- data markup
- training models with an expert
- improving quality on a small budget
Limitations
The method does not help if the initial data is bad or the expert labels inconsistently. It is important to ensure that the model does not become fixated on a narrow group of complex examples.