TLDR : I had successfully implemented ("exported" + tested + verified) mathematica's GradientBoostedTree PredictorFunction[] model in python.
By "exported", I mean thoroughly matched the each predicted values (mathematica vs python).
Here is the a simplified workflow :
[1] On the trained GradientBoostedTree (I name it 'p7mmv') PredictorFunction, do Information[p7mmv,"MethodOption"]
[2] referring to wolfram docs, found that it was an implmentation of https://lightgbm.readthedocs.io/en/latest/ .
[2a] Thus with exactly the same training & validation set, train the new model.
[2b] As for the setting use all info from [1], n apply it here https://lightgbm.readthedocs.io/en/latest/Parameters.html
[3] Run the prediction output and compare (all).
Just sharing on a success on my side ( and my model ). Any clarifications/comment is welcomed.