The Importance of Variable Importance
Methodology
2022-12-08 v1
Abstract
Variable importance is defined as a measure of each regressor's contribution to model fit. Using R^2 as the fit criterion in linear models leads to the Shapley value (LMG) and proportionate value (PMVD) as variable importance measures. Similar measures are defined for ensemble models, using random forests as the example. The properties of the LMG and PMVD are compared. Variable importance is proposed to assess regressors' practical effects or "oomph." The uses of variable importance in modelling, interventions and causal analysis are discussed.
Cite
@article{arxiv.2212.03289,
title = {The Importance of Variable Importance},
author = {Charles D. Coleman},
journal= {arXiv preprint arXiv:2212.03289},
year = {2022}
}
Comments
32 pages