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, which estimates influence for individual predictions. The framework typically employs perturbation-based methods, such as permuting a feature and measuring the resulting change in performance, as well as model-agnostic techniques that fit simple surrogates or compute sensitivity indices. It includes mechanisms to handle feature interactions and correlations, for example through conditional permutation or dependency-aware surrogates. Outputs can include importance scores, ranking, partial dependence plots, and confidence intervals. The computational load varies with sample size and model complexity.

Implementation commonly integrates with standard machine learning libraries and supports cross-validation, bootstrapping, and scalable computation. The resulting reports are intended for model developers and decision-makers, supporting feature selection, debugging, and policy analysis.

History and adoption: The term appears in academic and practitioner discussions in the 2020s. It is described as a generic concept rather than a single algorithm; various software packages label their methods as importansmodeller or offer compatible components.

See also: Feature importance, Sensitivity analysis, Permutation importance, SHAP, LIME.