datasubset

A datasubset is a subset of data drawn from a larger dataset, defined by criteria, sampling method, or both. It typically refers to a collection of records (rows) selected from the original dataset, possibly along with a subset of attributes (columns). In database terms, a datasubset can be produced by applying a query with a filter condition; in data science terms, it often means a sample used for analysis or model development.

Creation methods include random sampling, which selects records with equal probability; stratified sampling, which divides the population into strata and samples within each; and systematic sampling, which uses every k-th record. Filtering applies predicate conditions to preserve only records that meet criteria. Projection reduces dimension by selecting a subset of columns. Combination approaches are common.

Uses of datasubsets include supporting exploratory data analysis, debugging pipelines, rapid prototyping, and machine learning workflows. They are used for training or validating models, benchmarking algorithms, and performing privacy-preserving analyses with reduced data.

Considerations involve bias and representativeness, as subset selection can distort distributions or correlations if not done carefully. Reproducibility depends on fixed random seeds or explicit criteria. Subsets should be versioned and documented to ensure comparability across experiments. Privacy and compliance may require additional anonymization or data minimization.

Relation to related concepts: datasubsets are distinct from a feature subspace (subset of columns) and from a training/validation/test split, though these can be considered datasubsets depending on context. The term is context-dependent and may be used interchangeably with "sample" or "filtered dataset" in practice.