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.
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.
It's unclear what you mean by this part:
Now, that I have the results, is it possible to interpret this in some format where I can then implement the fit without using sklearn or even python?
Implement the fitting process for a given dataset? tree topology? choice of parameters?
As to 'implement... without using sklearn or python', did you mean 'port the bytecode or binary' or 'clean-code a totally new implementation'?
Assuming you meant the latter, I'd suggest GPU rather than FPGA or uC.
