I am dealing with very right-skewed distribution of y which seems to follow a tweedie distribution. and I found out it would give a higher performance to change lgbm's loss function to a "tweedie".
As far as I know, a boosting model is non-parametric which means it has no assumption regarding a distribution of data and at the same time, gradient boosting has a loss function which varies on the target distribution.
At this point, I kinda felt contradictory since it has no assumption of distribution but still its loss function depends on the target distribution..
Doesn't it have a direct connection to whether an algorithm is parametric or not that its loss funtion depends on a distribution of target variable?