I would like to implement various AI-estimators for quantile estimation for a regression problem. It would be necessary to have non-crossing quantiles, that is larger quantiles would correspond to higher prediction values.

My objective is to have a multidimensional prediction vector as an output in the estimation, each dimension corresponding to a specific quantile. Maybe I would have to define a custom loss function, as well, for that purpose. I would like to try different methods such as deeplearning or gradient boosting or random forests.

Anyone having an idea how to build such AI-estimators? My preferred library choice would be scikit-learn.

Can someone give me an idea how to do 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.