Featurelabel

Featurelabel is a metadata concept used in data science and machine learning to describe the semantic meaning and context of a feature beyond its technical name. It functions as a human- and machine-readable label that accompanies a feature, clarifying what the feature represents, its units, data type, and any domain-specific notes. In practice, featurelabels are stored in a feature dictionary, metadata registry, or feature store and are linked to the corresponding feature identifier.

The primary purpose of a featurelabel is to improve discoverability, governance, and reuse of features across projects. By providing a consistent description, teams can understand and compare features, assess their suitability for models, and trace provenance during experimentation and deployment. Featurelabels also support automated tooling, such as feature catalog generation, documentation, and lineage tracking, and they aid in model explainability by making feature semantics explicit.

Common elements of a featurelabel include the feature name or id, a human-readable description, the data type, units or scale, valid ranges or categories, source information, and any preprocessing notes. An example might label a feature with the id avg_temp as “Average daily temperature in degrees Celsius, derived from sensor readings.”

See also related concepts such as feature names, feature dictionaries, metadata registries, and feature stores. Limitations include the potential for drift if labels are not maintained or standardized across teams.