Surrogate minimal depth as an importance measure for variables in random forests.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Oct 1, 2019
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.
