i am currently experimenting with quantile-regression in h2o. I am building prediction intervals. For the individual regression models i am looking after R^2, RMSE and also quantile-loss. I performed a grid-search for the two quantile-models that build the lower and upper bound of the interval which improved R^2 (higher) and RMSE (lower). However, quantile-loss did increase for the optimized models, compared to the "base"-models.

I am now thinking of the role of quantile-loss and how to interpret this. I thought that quantile-loss would decrease as well when models are tuned.

Any thoughts or hints on this?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.