# xgboost or lightgbm to handle Binomial problems [duplicate]

I have a dataset containing a column of trials, a column of successes and other features; and, obviously, I can generate a probability column. I would like to use gradient boosting methods (like xgboost or lightgbm) to model the success probability. Which parameter shall I set to handle this in lightgbm or xgboost?

Also see this post for a very similar problem. As mentionned there, a naive approach could be to simply run a normal regression in a boosting setup and see how "bad" the results are (i.e. values $$<0$$ and $$>1$$). Alternatively you could try to predict the continuous features and calculate the success probability from predicted values.