Article

Forward variable selection improves the power of random forest for high- dimensional microbiome data

2020-10-30

Abstract excerpt

<h4>Background</h4> Random forest (RF) captures complex feature patterns that differentiate groups of samples and is rapidly being adopted in microbiome studies. However, a major challenge is the high dimensionality of microbiome datasets. They include thousands of species or molecular functions of particular biological interest. This high dimensionality significantly reduces the power of random forest approaches...

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Literature Corpus work: 73c7b29a-7f40-5d72-975a-a6fa2889edbc
DOI: 10.1101/2020.10.29.361360

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