POSTfel

POSTfel, short for Post-Feature Evaluation Language, is a formal language and framework designed to specify, validate, and automate post-feature evaluation steps in machine learning pipelines. It aims to separate feature engineering decisions from the modeling phase, improving reproducibility, auditability, and governance by encoding evaluation criteria and transformations as explicit rules.

The core concepts of POSTfel include a feature-set schema, evaluation rules (metrics, thresholds, and relationships), transformation blocks (scaling, encoding, interaction generation), and provenance mechanisms for logging changes and decisions. The language is designed to be declarative, so pipelines can derive post-feature results from the same inputs across environments and over time, aiding comparison and traceability.

Architecturally, POSTfel provides a compact syntax that can be parsed by a dedicated interpreter or compiler and integrated with existing data pipelines and feature stores. It emphasizes interoperability, versioning, and the ability to roll back to prior rule sets. It can express constraints for fairness, privacy, and compliance as part of the evaluation step, enabling governance to be baked into feature processing rather than appended later.

Status and usage: POSTfel emerged in academic and industry discussions in the mid-2020s as a concept to improve reproducibility in ML projects. While not yet standardized or widely adopted, it is used in demonstrations and prototype tools to illustrate how explicit post-feature governance can be encoded.

See also: declarative data pipelines, feature stores, model governance, formal languages.