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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?

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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]).

An easy way to have implemented this would have been to allow the Beta distribution (see how it is bounded) as an objective function in xgboost, but it is not an option at this point.

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