Description: Uncertainty sampling is a strategy used in the field of machine learning, particularly in semi-supervised learning and active learning. Its main goal is to select the most informative data points for labeling, which enhances the efficiency of the training process. This technique is based on the idea that not all data is equally useful; some may provide more information about the target function than others. By identifying and prioritizing these points, the number of samples needed to achieve optimal model performance can be reduced. Uncertainty sampling can be implemented in various ways, such as by evaluating the uncertainty in the model’s predictions using metrics like entropy or prediction variance. This methodology not only optimizes resource usage but can also lead to more robust and accurate models, as it focuses on learning from the most challenging and representative examples in the dataset. In the context of machine learning systems, uncertainty sampling can be particularly valuable, given the variability in data across different applications and domains, where the selection of informative examples can significantly impact model performance.
