# Getting prediction intervals from GBM models

I am working with GBM regression models(in H2O) and am using Quantile distribution for the distribution parameter. I am looking for a method to provide prediction intervals in addition to point value prediction. What is the best way to achieve this generally?

Since we are using quantile regression, I was hoping we should be able to exploit it as follows: Lets say we are interested to find 95% prediction interval. We train one GBM model with quantile alpha=.025 and another with .975. And then we call predict on these 2 models to get lower and upper values of the range. Does this approach seem correct?