colorderived

Colorderived is an adjective used in data science and database theory to describe data, features, or metrics that depend on the order of columns in a data structure rather than solely on the values of those columns. A colorderived quantity may change if the columns are reordered, even if the underlying cell values remain the same.

Origins and usage: The term is informal and not widely standardized; it appears mainly in theoretical discussions about columnar data organization and in some niche software documentation. It contrasts with row-derived or value-derived constructs, which depend on row indices or the data values themselves, respectively.

Properties: Colorderived objects are sensitive to column permutation and often require a canonical or maintained column order to be interpretable. They may complicate data exchange and reproducibility when datasets are restructured.

Examples: A simple colorderived feature is the sum of the first three columns in every row, where the feature's meaning depends on which columns occupy positions 1–3. A more formal example is a meta-feature encoding the position of a non-null value in each row's column sequence.

Relation to practice: In machine learning pipelines and database queries, recognizing colorderderived elements helps manage permutation invariance and data lineage. When column order changes, colorderived results should be recomputed or the original order preserved via metadata.

See also: column order, permutation invariance, feature engineering, data provenance.