# Active Learning

> Iteratively selects the most informative samples for labeling to maximize model improvement.

- **Category**: Data Science
- **Subcategory**: ML Workflows
- **Canonical URL**: https://designpattern.fyi/patterns/active-learning/

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## Description
**Context**: Labeling all data is expensive. Active learning identifies uncertain or informative samples for human labeling, reducing labeling costs while maximizing model performance.


## Use Cases
ML projects with limited labeling budget where you need to maximize model performance with minimal labeled data.



## Implementation Example

```python
# Active Learning Pattern class ActiveLearner: def __init__(self, model, strategy): self.model = model self.strategy = strategy
def select_samples(self, unlabeled_pool, n_samples): # Score uncertainty for unlabeled samples scores = self.strategy.score(unlabeled_pool, self.model)
# Select most uncertain samples selected_indices = np.argsort(scores)[-n_samples:]
return unlabeled_pool[selected_indices]
def update_model(self, labeled_samples): self.model.fit(labeled_samples)
# Use uncertainty sampling strategy learner = ActiveLearner(model, UncertaintySampling())
```



## Trade-offs


### Advantages

- - Reduced labeling cost

- - Faster model improvement

- - Focuses on informative samples

- - Efficient resource use




### Considerations & Drawbacks

- - Selection strategy complexity

- - Computational overhead

- - May miss rare classes

- - Implementation complexity