I am using Random Forest Regression (with Python sklearn, but could easily switch to R if that would work better) to predict a variable. I think my model is starting to do fairly well, however I see a very clear pattern in the error: the residual error is highly correlated with distance from the mean value. Now I understand that that is somewhat the point of using a forest: to increase bias over variance. I can obviously increase variance again by reducing the number of trees that are used, but that also increases overall error. Is there a better way of doing this?