I'm running a random forest model on a training sample in R in order to make predictions on a hidden test set. I'm having difficulty in understanding how I should go about improving my model in order to make better predictions on the test set. There are a number of variables I can add to or remove from the model, but at the moment I am having to make decisions based on logic and gut feeling as opposed to using a formal method.
As I understand it, the training set AUC for random forests is computed on the out-of-bag samples, so assuming that the nature of the data in the test set is similar to that of the training set, should I be looking to optimize this value?
Does the out-of-bag error estimate have any bearing on how the model will perform on the test set? Does a lower value here indicate a better model that would be expected to perform better on the test set?
Any advice would be greatly appreciated!