Modellekig

Modellekig is a term used in theoretical discussions of model integration in data science, describing a modular framework for combining multiple predictive models into a single, coherent system. The concept emphasizes interoperability among components and the reusability of submodels, rather than a single end-to-end algorithm.

Architecture and principles: A modellekig system typically comprises four layers: data processing, a model layer with heterogeneous learners, an orchestration layer that coordinates predictions, and a calibration layer that reconciles outputs. Interfaces are designed to be modular and interoperable to allow plug-and-play of diverse models. The ensemble can use approaches such as weighted averaging, stacking, or conditional routing.

Origins and use: The term does not refer to a specific product or standard; it appears in discussions and speculative case studies within the broader discourse on ensemble and modular AI. It is used to illustrate how modular design can improve scalability, maintainability, and interpretability when integrating diverse modelling approaches.

Evaluation and limitations: Critics caution that modellekig can introduce complexity, risk of information leakage across components, and calibration challenges when submodels have different data distributions. Rigorous evaluation requires cross-component validation, careful data handling, and transparent auditing of the orchestration logic.

Relation to related ideas: It is related to ensemble learning, modular AI, and mixture-of-experts approaches, but differs in its emphasis on standardized interfaces and explicit calibration across modules.