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/ . 

     a) with exactly the same training & validation set, train the new model.

     b) 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 wish to share on a (small) successful mathematica trained model re-make story.. Any clarifications/comment/improvements is welcomed.