Active learning uses model predictions to prioritize which data to label next, maximizing the value of each annotation and reducing overall labeling costs.
What is Active Learning for Data Labeling?
Active learning identifies the most informative samples for annotation. Instead of labeling data randomly, focus your annotation budget on samples where your ML model is most uncertain.
Active Learning Strategies
- Uncertainty Sampling - Label samples with highest model prediction uncertainty
- Diversity Sampling - Select diverse samples to cover the feature space
- Expected Model Change - Choose samples that would most improve the model
- Query by Committee - Use multiple models to identify disagreement
Implementing Active Learning
- Train initial model on your existing labeled training data
- Run inference on unlabeled data pool
- Score samples by uncertainty or informativeness
- Send top-N samples to TigerLabel for annotation
- Add new labels to training set and retrain
- Repeat until model performance plateaus
Pro Tip: Active learning can reduce annotation costs by 50-80% while achieving the same model performance as labeling all data randomly.