Active learning optimizes machine learning by strategically selecting which data to label, reducing annotation costs while maintaining model performance. This approach is crucial for applications where labeled data is expensive or scarce.

How Active Learning Works

• Uncertainty Sampling: Select examples the model is least confident about for labeling.

• Query Strategies: Choose informative samples that improve model performance most.

• Iterative Process: Train model, identify uncertain examples, obtain labels, retrain.

• Human in the Loop: Expert annotators label strategically selected data.

• Budget Optimization: Achieve target performance with minimal labeling cost.

Query Strategies

Uncertainty sampling prioritizes ambiguous examples, diversity sampling ensures representative coverage, expected model change selects impactful samples, query by committee uses ensemble disagreement, and density-weighted methods balance informativeness with representativeness.

Applications

Medical imaging reduces radiologist annotation burden, text classification optimizes document labeling, fraud detection focuses on suspicious transactions, quality control identifies defect patterns efficiently, and autonomous systems learn from critical scenarios.

Benefits

Reduce labeling costs by 50-90% compared to random sampling, accelerate dataset creation through focused annotation, improve model performance with less data, enable rapid prototyping with limited budgets, and adapt to changing data distributions efficiently.