importansmodeller
Importansmodeller is a framework used in the field of model interpretability to quantify the relative importance of input features for a predictive model. It provides a structured approach to assess how changes to input variables influence model outputs, enabling analysts to identify which features drive predictions and to communicate model behavior to stakeholders.
Core concepts include global feature importance, which aggregates influence across a dataset, and local feature importance,
Implementation commonly integrates with standard machine learning libraries and supports cross-validation, bootstrapping, and scalable computation. The
History and adoption: The term appears in academic and practitioner discussions in the 2020s. It is described
See also: Feature importance, Sensitivity analysis, Permutation importance, SHAP, LIME.