Active Learning
by Community · free · Last verified 2026-03-17
Active Learning is a machine learning technique that intelligently selects the most informative data points from a large pool of unlabeled data to be labeled by a human annotator. By prioritizing examples where the model is most uncertain, it aims to achieve higher model accuracy with significantly fewer labeled samples, reducing annotation costs and time.
https://modal.com/docs ↗B
B—Above Average
Adoption: B+Quality: AFreshness: ACitations: B+Engagement: F
Specifications
- License
- MIT
- Pricing
- free
- Capabilities
- uncertainty-sampling, query-by-committee, expected-model-change, expected-error-reduction, core-set-selection, diversity-sampling, bayesian-active-learning-by-disagreement, batch-aware-selection, model-outlier-detection
- Integrations
- scikit-learn, modAL, libact, PyTorch, TensorFlow, Labelbox, Prodigy, SuperAnnotate
- Use Cases
- [object Object], [object Object], [object Object], [object Object]
- API Available
- No
- Difficulty
- intermediate
- Prerequisites
- machine-learning, statistics, data-annotation
- Supported Agents
- Tags
- active-learning, data-labeling, annotation, query-strategy, low-resource, human-in-the-loop, semi-supervised-learning, data-efficiency, model-training, labeling-efficiency, smart-sampling
- Added
- 2026-03-17
- Completeness
- 1%
Index Score
63.2Adoption
70
Quality
82
Freshness
80
Citations
75
Engagement
0