This question pertains to L1 & L2 regularization parameters in Light GBM. As per official documentation:
reg_alpha (float, optional (default=0.)) – L1 regularization term on weights.
reg_lambda (float, optional (default=0.)) – L2 regularization term on weights
I have seen data scientists using both of these parameters at the same time, ideally either you use L1 or L2 not both together.
While reading about tuning LGBM parameters I cam across one such case: Kaggle official GBDT Specification and Optimization Workshop in Paris where Instructors are ML experts. And these experts have used positive values of both L1 & L2 params in LGBM model. Link below (Ctrl+F 'search_spaces' to directly reach parameter grid in this long kernel)
I have seen same in XGBoost implementations.
My question is why use both at the same time in LGBM/XGBoost.