I have a classification problem with approximately 1000 positive and 10000 negative samples in training set. So this data set is quite unbalanced. Plain random forest is just trying to mark all test samples as a majority class.
Some good answers about sub-sampling and weighted random forest are given here: What are the implications for training a Tree Ensemble with highly biased datasets?
Which classification methods besides RF can handle the problem in the best way?