Having a fitted quantile regression (forest) model is great. However, how does one choose the best quantile to perform the actual prediction?
One idea would be to use bootstrapping. In other words, re-sample from the (training?) data and calculate the error distribution (e.g. RMSE) for different quantiles. The distribution of the quantile with the lowest mean/median error (and smallest variance?) could be used to predict.
What do you think? Any other input would be very much appreciated.