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?
I do not think this is possible with the main (as I can tell) xgboost library, because this essentially sounds like a regression on rates/proportions situation (a dependent variable bounded in [0,1]).
There are beta regression models for R and Python. They are designed to handle values between 0 and 1. However, as far as I'm aware, there is no direct beta regression implementation with LightGBM, XGboost etc. Some folks seem to expand beta regression into tree-based approaches, but this is not equivalent to boosting.
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.