interfeature

Interfeature is a concept used to describe the interactions between features in a system, dataset, or product configuration. It refers to the non-additive influence that combinations of features exert on an outcome, beyond the sum of individual effects.

In data science and machine learning, interfeature analysis aims to detect and model these interactions to improve predictive performance and to gain insight into feature dependencies. Methods include adding explicit interaction terms in linear models, using tree-based models that partition on feature combinations, and model-agnostic measures such as SHAP interaction values. Statistical tests and information-theoretic metrics may also be employed to quantify interaction strength.

In software engineering and product management, interfeature describes dependencies, conflicts, or synergies between features within a configuration or feature model. Enabling one feature may affect cost, feasibility, or behavior of another, potentially causing interaction bugs or inconsistent user experiences. Practices to manage interfeature effects include dependency graphs, configuration validation, regression testing across feature combinations, and careful feature-toggle design.

The term is descriptive rather than normative and is used in multiple disciplines with varying specificity. It often functions as a label for cross-feature effects rather than a formal specification. See also: feature engineering, feature interaction, interaction term, feature flags, dependency graph, combinatorial testing.