Uncertainty Sampling

Uncertainty Sampling is an active learning technique used in machine learning to select the most informative data points for labeling from an unlabeled dataset. It focuses on instances where the model's predictions are most uncertain, such as those with low confidence scores or high entropy in predicted probabilities. This approach aims to improve model performance efficiently by reducing the amount of labeled data required for training.

Also known as: Uncertainty-based Sampling, Active Learning Sampling, Query-by-Uncertainty, Least Confidence Sampling, Entropy Sampling

Why learn Uncertainty Sampling?

Developers should use Uncertainty Sampling when working with limited labeled data budgets, such as in supervised learning tasks where labeling is expensive or time-consuming. It is particularly valuable in domains like natural language processing, computer vision, and medical imaging, where expert annotation is costly. By prioritizing uncertain samples, it accelerates model convergence and enhances accuracy with fewer labeled examples.

Learning Resources

• docs: Active Learning Literature Survey
• tutorial: Scikit-learn Active Learning Tutorial
• video: Uncertainty Sampling in Deep Learning - YouTube
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Chapter on Active Learning