I’m doing a regression on a dataset using lightgbm. For the training data the response variable has a non normal distribution which is multimodal. However, the predictions out-of-fold have are normally distributed and have a single mode. Is there an assumption of normality made by light gbm ? Is there a way to make predictions which better match the required distribution?