Evaluation of variable selection methods for random forests and omics data sets.

Abstract

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction… (More)
DOI: 10.1093/bib/bbx124

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