Random Forest interpretation in scikit-learn

I am using scikit-learn's Random Forest Regressor to fit a random forest regressor on a dataset. Is it possible to interpret the output in a format where I can then implement the model fit without using scikit-learn or even Python?

The solution would need to be implemented in a microcontroller or maybe even an FPGA. I am doing analysis and learning in Python but want to implement on a uC or FPGA.

Answers


You can check out graphviz, which uses 'dot language' for storing models (which is quite human-readable if you'd want to build some custom interpreter, shouldn't be hard). There is an export_graphviz function in scikit-learn. You can load and process the model in C++ through boost library read_graphviz method or some of other custom interpreters available.


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