Surrogate minimal depth as an importance measure for variables in random forests.

ABSTRACT:

Motivation

It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult.

Results

Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improve

SUBMITTER: Seifert S 

PROVIDER: S-EPMC6761946 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature