Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification
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arXiv:2610.33307v2 Announce Type: new Abstract: Boruta is a widely used method for selecting gene panels from RNA sequencing data, and its most used implementation recommends Random Forest trees of depth 3 to 7, an untested setting. We tested whether this setting changes which genes are selected, u…
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