factormust

Factormust is a term used in decision science and artificial intelligence to describe a family of constraints that determine whether a factor, or feature, in a model is admissible. A factor considered under the factormust concept must satisfy a predefined set of mandatory properties before it can influence predictions or decisions. The property set is not fixed; it is defined by the problem context and can include measurability, interpretability, stability, and causal plausibility, among others. In practice, factormust criteria function as a gatekeeping mechanism in feature selection and model auditing, ensuring that inputs are credible and explainable.

In typical use, practitioners enumerate the factormust requirements for their project, evaluate candidate features against them, and retain only those that meet all criteria. This reduces overfitting risk, improves transparency, and supports regulatory compliance in high-stakes domains such as finance and healthcare. Some approaches formalize factormust as a predicate or checklist, while others embed the constraints into optimization objectives or regularization terms.

Criticism notes that the concept can be subjective and domain-dependent. Overly strict or poorly defined factormust criteria may suppress useful signals or introduce bias, and the evaluation can add computational overhead. Proponents argue that a clear factormust framework can improve trust and accountability in data-driven systems.

See also: feature selection, model validation, constraint-based learning, model transparency.