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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 sharing on a success on my side ( and my model ). Any clarifications/comment is welcomed.

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.

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 sharing on a success on my side ( and my model ). Any clarifications/comment is welcomed.

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 sharing on a success on my side ( and my model ). Any clarifications/comment is welcomed.

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.

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 sharing on a success on my side ( and my model ). Any clarifications/comment is welcomed.