interactionsfull

interactionsfull is a term used in several disciplines to denote a comprehensive accounting of interactions within a system. Because it is not tied to a single field, it lacks a universal definition and is used flexibly in different contexts.

In data science and analytics, interactionsfull may refer to approaches that aim to capture all pairwise and higher-order interactions among variables, agents, or components. This can involve collecting detailed event data, constructing interaction graphs, or employing models that include interaction terms or tensor representations to summarize combined effects.

Applications include sociotechnical systems, epidemiology, and recommendation engines where understanding how factors interact improves predictions or insights.

Techniques include polynomial or spline-based interaction terms, generalized additive models, factorization machines, and tensor factorization. Challenges include combinatorial growth of potential interactions, data sparsity, overfitting, interpretability, and privacy concerns.

Critics note that the broad term can obscure methodological choices; practical use typically requires clear scope, principled regularization, and validation.

See also: interaction terms, higher-order interactions, full interaction models.