feldata

Feldata is a term used in certain data science discussions to describe a lightweight, line-oriented data representation intended for feature-focused machine learning workflows. It is not a widely adopted standard, and the term can refer to diverse, project-specific conventions.

In typical descriptions, a feldata record consists of an identifier, a label or target field, and a collection of features. The feature portion may be encoded as a flat key-value map or as a compact vector with fixed length; numeric features are stored as numbers, while categorical features may be one-hot encoded or hashed. The format favors streaming processing, as records can be appended to a file or stream and parsed incrementally.

Common uses include educational tutorials illustrating feature engineering, lightweight data exchange between components in a machine learning pipeline, and demonstrations of feature-logging for experiments. Because feldata is not standardized, there is considerable variance in field names, delimiter choices, and handling of missing values.

Relation to other formats: Feldata often resembles JSON Lines or CSV variants, or simple JSON-based line-delimited formats, but with a focus on the feature subset. It may be implemented as a straightforward wrapper around existing line-delimited formats or as a project-specific convention.

Limitations and considerations: Interoperability is limited across tools; the lack of formal specification can hinder validation and tooling. When employing feldata in real projects, practitioners are advised to document schema, delimiter, and feature encoding to facilitate reuse and collaboration.