Exploring the variable importance in random forests under correlations: a general concept applied to donor organ quality in post-transplant survival.

ABSTRACT: Random Forests are a powerful and frequently applied Machine Learning tool. The permutation variable importance (VIMP) has been proposed to improve the explainability of such a pure prediction model. It describes the expected increase in prediction error after randomly permuting a variable and disturbing its association with the outcome. However, VIMPs measure a variable's marginal influence only, that can make its interpretation difficult or even misleading. In the present work we address the general need for improving the explainability of prediction models by exploring VIMPs in the presence of correlated variables. In particular, we propose to use a variable's residual information for investigating if its permutation importance partially or totally originates from correlated predictors.

SUBMITTER: Wies C 

PROVIDER: S-EPMC10507897 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature