Hi all, I'm using sklearn Randon Forest Regressor. I have trained the model in Python and dumped the model using Pickle. The model is used inside C++ application for prediction. Currently I have implemented predict function using "Embedding Python in C++" concept. However, the problem is, it is causing huge runtime overhead and I can't afford that much runtime.
Is there a way, can I extract the decision tree information and RF parameters from RF trained model? I can implemented the decision trees with RF parameters in C++. If you aware of alternative directions, please let me know. I would appreciate it. Thanks. -- Regards, Chidham
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