Journal Article10.1002/WIDM.12
Multivariate random forests
Mark R. Segal,Yuanyuan Xiao +1 more
259
TL;DR: The genesis of, and motivation for, the random forest paradigm as an outgrowth from earlier tree‐structured techniques is outlined and an illustrative example from ecology is provided that showcases the improved fit and enhanced interpretation afforded by the random Forest framework.
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Abstract: Random forests have emerged as a versatile and highly accurate classificationand regression methodology, requiring little tuning and providing interpretableoutputs. Here, we briefly outline the genesis of, and motivation for, the randomforest paradigm as an outgrowth from earlier tree-structured techniques. Weelaborate on aspects of prediction error and attendant tuning parameter issues.However,ouremphasisisonextendingtherandomforestschematothemultipleresponse setting. We provide a simple illustrative example from ecology thatshowcases the improved fit and enhanced interpretation afforded by the randomforest framework.
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Andy Liaw,Matthew C. Wiener +1 more
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TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
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