# Validation in Statistics and Machine Learning - Abstract

**Grömping, Ulrike**

*Variable Importance in Linear Models and Random Forests*

In many regression contexts, with regression defined in its broadest sense, researchers are interested in variable importance, either for simplified comparison between regressors in a given model or for supporting variable selection. Variable importance metrics in linear models and random forests have a lot of common ground. The nature of variance-based variable importance metrics is investigated for the linear model, simulation comparisons to random forest variable importance are presented, and requirements for the adequacy of variable importance metrics are discussed.