Multi-parameter optimization with custom loss function for probabilistic forecasting about lightgbmlss

[javier-cazana]

have a look here to see how I did it using the Focal loss, which depends on two parameters, alpha and gamma. I used hyperopt as the optimization routine:

https://github.com/jrzaurin/LightGBM-with-Focal-Loss/blob/master/utils/train_hyperopt.py

[javier-cazana]

@StatMixedML

can you not code a custom loss and include it in the optimization loop?

if I have time later I will paste some code

[StatMixedML]

@javier-cazana Nice, let me go through it.

Also referring to the discussion we are having here: StatMixedML/XGBoostLSS#8 (comment)

[2533245542]

This paper "NGBoost: Natural Gradient Boosting for Probabilistic Prediction"
https://arxiv.org/pdf/1910.03225.pdf
assumes a distribution with parameters on p(y|x) and optimizes the parameters using gradient descent. Does this help? Ignore me if it does not.

[StatMixedML]

@2533245542 Many thanks for the paper link.

I do know the paper very well. The problem is that we need to find a proper way to translate the model training of LightGBM into multi-parameter training. Not quite sure how to do that. Any suggestions?

[StatMixedML]

closing since issue is resolved