samplesmost

Samplesmost is a term used in data science and machine learning to describe a subset of data points selected to best represent the full dataset under a specified objective. The concept is not codified in standard statistics; definitions vary by domain. In practice, samplesmost prioritizes preserving distributional properties and predictive utility while reducing data volume.

Common formulations aim to minimize differences between the full and reduced datasets. Techniques include stratified sampling to preserve class balance, clustering-based core-set construction in which representatives from each cluster are retained, and submodular optimization that balances coverage against redundancy. Some approaches use distance or divergence measures such as KL divergence, Wasserstein distance, or feature-coverage metrics as objectives.

Applications include efficient model training and evaluation with limited resources, rapid prototyping, and dataset curation for deployment where data collection is costly. In imbalanced or rare-event settings, a carefully selected samplesmost can help maintain detection rates while reducing computational load. The concept is related to core-sets and condensed representations used in active learning and data summarization.

Evaluation of a samplesmost subset is inherently task-dependent; common benchmarks include downstream accuracy, calibration, and robustness, as well as how well distributional properties are preserved. Challenges include definitional ambiguity, potential biases in the selection process, and the risk that a subset optimized for one objective does not generalize to others. Further standardization remains limited as the term spans multiple interpretations. See also core-set, dataset condensation, active learning.