My LightGBM regressor model returns negative values.

For XGBoost there is objective='count:poisson' hyperparameter in order to prevent returning negative predicitons.

Is there any chance to do this ?

Github issue =>

However... I don't recommend using Poisson regression just to achieve "no negative predictions". The Poisson loss function is intended to be used for cases where you believe your target is Poisson-distributed, e.g. it looks like counts of events observed over some regular interval like time or space.

Other options you might consider to try to achieve the behavior "never predict a negative number from LightGBM regression":

• write a custom objective function in one of the interfaces that support it, like the R or Python package
• post-process LightGBM's predictions, recoding negative values to 0
• pre-process the target variable such that there are no negative values (e.g. dropping such observations, re-scaling, taking the absolute value)