Featurelabels

Featurelabels refer to descriptive metadata attached to features in datasets, feature stores, and machine learning pipelines. They pair a feature identifier with human-readable information that clarifies what the feature represents, its units, data type, valid values, and provenance. Featurelabels help bridge the gap between technical feature names and domain understanding, supporting interpretability, documentation, and governance.

A typical featurelabel includes several elements: display name or label, a description clarifying the feature’s meaning, data type and unit (if applicable), allowed or typical value ranges, and provenance such as data source and calculation method. Some implementations also track version, last updated timestamps, and data quality notes. In practice, featurelabels form a lightweight metadata layer that accompanies features in data pipelines and feature catalogs.

Purpose and benefits include improved model explainability, easier collaboration between data scientists and domain experts, and better auditability for regulatory or quality-control requirements. They enable consistent naming across teams, simplify feature reuse, and assist automated reporting and lineage tracking. Featurelabels are often managed within data catalogs or feature stores and can be synchronized with data dictionaries, schemas, or ontology references.

Applications range from documenting features in ML projects to enriching dashboards and automated model documentation. They are commonly integrated with feature stores (for example, to provide human-readable names alongside raw feature IDs), data governance tools, and data discovery platforms. Challenges include maintaining consistency across versions, handling multilingual labels, and avoiding ambiguity when domain concepts evolve.

Example: a feature with id age_in_days could have a featurelabel "Age" with description "Age of individual in days" and unit "days"; gender_code could have "Gender" with mapping 0=Male, 1=Female. See also data dictionary and feature store.