Active Learning
Active Learning is a semi-supervised learning framework where a machine learning algorithm queries a human annotator to label only the most informative or uncertain data points, minimizing labeling cost.
Frequently Asked Questions
What is uncertainty sampling in Active Learning?▼
An active learning strategy where the model requests labels for the data points it is least confident about (e.g., classifications close to the decision boundary).
Why is Active Learning useful?▼
Labeling data is often the most expensive and time-consuming part of machine learning. Active learning achieves high model performance with a fraction of the labeled data.
Quick Facts
- CategoryModel Training
- Key ApplicationAnnotating rare medical images, building high-quality natural language processing training sets, and selective data labeling budgets.
Coverage Trend12 Weeks
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