# How (not) to (over)fit a random forest in R

I'm reaching out to you because I am unsure whether my implementation of a group of random forests in R (using library randomForest) is valid or whether I have an error in reasoning.

I have a sales dataset with a binary outcome (1: Sale, 0: No Sale) and a set of possibly significant predictors x1-x14. My data is highly imbalanced, with ~124k '0' observations (No Sale) and ~18k '1' observations (Sale). I balance it by randomly cutting down the 124k observations to 18k, as suggested in http://bit.ly/1I7F0AC.

Cross-validation is not necessary due to the nature of random forests, however: In order to find a random forest with a good F-score, I loop through a set of possible predictors and a set of tree-numbers for the forest:

possiblyUsefulPredictors=
c("x1",..."x14") # Shortened to pseudo-code

treerange=c(1,2,3,4,5,6,7,8,9,10,15,20,25,30,35,40,45,50,60,70,80,90,100,
200,300,400,500,750,1000)
# Create a multitude of models by looping
# through different settings for parameters
for (i in 2:length(possiblyUsefulPredictors)){
for (j in treerange){

### Choose model here by setting data, outcome and predictors:
x=possiblyUsefulPredictors[1:i] # Set predictors
ntree=j # Set number of trees
# Tune mtry
bestMtry=tuneRF(x=x, y=y, ntreeTry=1,
stepFactor=1, improve=0.01, trace=FALSE,
plot=FALSE, doBest=FALSE)
# Run random forest
rf=randomForest(y=y,x=x,data=df,mtry=bestMtry,ntree=ntree,
type="classification",importance=T)
}
}


I then store model diagnostics precision, recall, and F-score in a table and choose the model that created the highest F-score (13 predictors, 90 trees, mtry=1, which leads to an F-score of 78%).

Specific questions:

1. Obviously, the way I subset and loop through the predictors is highly arbitrary. Could a more sophisticated approach (e.g. looping through all possible subsets) get me anywhere, or does a random forest inherently choose significant predictors, so that I wouldn't have to try to find a meaningful subset myself (like I do when using step-wise in linear regression)?

2. By building a set of 416 random forests, do I simply overfit the dataset? I am skeptical that the predictors are as good as my best model suggests.

Thank you and kind regards, Jan