RF tree size
The number of trees in Random Forest is suggested to be high enough in order to ensure that every input sample gets predicted at least a few times. It is suggested that if want auxiliary information like variable importance or proximity , grow a large number of trees is agg choice, since more stable results can be obtained. Well for the current testing data, I have seen that it is not straight proportional to the performance. Features from POS and NEG classes are not containing very good discrimination information, so even a very complex tree can not save the performance.