Improved high-dimensional prediction with Random Forests by the use of co-data.

ABSTRACT:

Background

Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary 'co-data' can be used to improve the performance of a Random Forest in such a setting.

Results

Co-data are incorporated in the Random Forest by replacing the uniform sampling probabilities that are used to draw candidate variables by co-data moderated sampling probabilities. Co-data here are defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate the co-data moderated Random Forest (CoRF) with two examples. In the first example we ai

SUBMITTER: Te Beest DE 

PROVIDER: S-EPMC5745983 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature