Trees and forests

For my ACT6100 weekly quiz, I usually generate some datasets, and then ask students to compare various predictive algorithms. Last week, it was about classification trees and random forests. And students were surprised to have such differences (they had to estimate the probability to have a specific label, for the barycenter of the covariates). Usually, I use the following to generate some (here 12) covariates that could be correlated library(FactoMineR) n=279 library(clusterGeneration) library(mnormt) k=12 S=genPositiveDefMat(“unifcorrmat”,dim=k) X=round(rmnorm(n,varcov=S$Sigma)+8,2) …