Diversity Sampling
Diversity sampling is a statistical and machine learning technique used to select a subset of data points that maximizes diversity or coverage of the underlying data distribution. It aims to ensure that the sampled data represents the full range of variations, patterns, or characteristics present in the original dataset, rather than just random or clustered selections. This method is commonly applied in areas like active learning, data annotation, and dataset curation to improve model robustness and reduce bias.
Developers should learn diversity sampling when working on machine learning projects that require efficient data labeling, model training with limited data, or mitigating dataset bias. It is particularly useful in active learning scenarios where you want to select the most informative data points for annotation, in creating balanced training sets for classification tasks, or when curating datasets for fairness and representativeness in AI applications. This helps reduce annotation costs, enhance model generalization, and ensure ethical AI practices.