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randomForest [R code]

SUPERVISED MACHINE LEARNING: Classify female puberty status by machine learning

In this project I apply the 'random forest' machine learning algorithm to the Bergen Growth Study 2 [vekststudien.no] female dataset, in order to predict Tanner breast stage from the endocrine profile

To plot the 'prevailing' model decision tree: options(repos='http://cran.rstudio.org') have.packages <- installed.packages() cran.packages <- c('devtools','plotrix','randomForest','tree') to.install <- setdiff(cran.packages, have.packages[,1]) if(length(to.install)>0) install.packages(to.install)

library(devtools) if(!('reprtree' %in% installed.packages())){ install_github('araastat/reprtree') } for(p in c(cran.packages, 'reprtree')) eval(substitute(library(pkg), list(pkg=p))) Then go ahead and make the model and tree:

library(randomForest) library(reprtree)

model <- randomForest(Species ~ ., data=iris, importance=TRUE, ntree=500, mtry = 2, do.trace=100)

reprtree:::plot.getTree(model)