I am trying to train a Random Forest classifier on a binary classification problem but with a highly imbalanced dataset where the positive class is much smaller than the negative class . I have undersampled my dataset so that both classes have equal proportions and did 10 fold cross validation over this dataset.
However I am having bad results (averaged over the 10 folds):
- recall : 46%
- precision : 54%
- accuracy : 53%
I was wondering if oversampling the positive class would yield better results. What would be the benefits of undersamling versus oversampling?