I'm specifically referring to Random Forest regression.
The first statistics that are usually printed after running a random forest regression (in R -
randomForest package -
Mean of squared residuals and
% Var explained obtained.
Tuning the model, if you made a "good" change, usually you get a lower
Mean of squared residuals and a higher
% Var explained: is this always the case? (I'm aware of the fact that
randomForest reports the variation and not the variance explained as specified here -> Manually calculated $R^2$ doesn't match up with randomForest() $R^2$ for testing new data).
In case it is not, should I prefer a lower
Mean of squared residuals or a higher
% Var explained?