Tree depth and sample-quality–associated features influence Boruta gene selection for RNA-Seq classification
Read the original at arxiv.org→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...
Original headline: "Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification"
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- Oct 27, 04:00 UTC arXiv cs.LG lead source Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification