I'm puzzled about why a dependent variable with the weakest correlation to the outcome variable emerges as the most important factor when I run my random Forest on the same dataset. It beats out factors with much stronger correlations with the outcome variable.
Now I'm not sure whether to trust my random Forest. All the predictor variables were numeric and the outcome variable was a factor variable with two levels.
Do you have insight about 1) why this happens and 2) what I should do next to assess which variables are in fact most predictive?
The RF code I used was:
rf_mod <- randomForest(Stable ~ ADF + EstAnnualVal + PR_Score + stdv_demand_intervals, data=var1, ntree=501, importance=TRUE)