Improved high-dimensional prediction with Random Forests by the use of co-data.
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ABSTRACT:
SUBMITTER: Te Beest DE
PROVIDER: S-EPMC5745983 | biostudies-literature | 2017 Dec
REPOSITORIES: biostudies-literature
Publications
Improved high-dimensional prediction with Random Forests by the use of co-data.
Te Beest Dennis E DE Mes Steven W SW Wilting Saskia M SM Brakenhoff Ruud H RH van de Wiel Mark A MA
BMC bioinformatics 20171228 1
<h4>Background</h4>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.<h4>Results</h4>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 t ...[more]
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