CODEWITHSUNDEEP
pythonmachine-learningregressionscikit-learnrandom-forest
Amol Desai
Amol Desai

Reputation: 117

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.

Upvotes: 6

Views: 3163

Answers (4)

Jon Nordby
Jon Nordby

Reputation: 6324

For a microcontroller implementation, it is usually most practical to use C. There are a few libraries that support converting a scikit-learn RandomForestRegressor to C code, including:

Upvotes: 0

G Roshan Lal
G Roshan Lal

Reputation: 31

You could trying extracting rules from the tree ensemble model and implement the rules in hardware.

You can use TE2Rules (Tree Ensembles to Rules) to extract human understandable rules to explain a scikit tree ensemble (like GradientBoostingClassifier). It provides levers to control interpretability, fidelity and run time budget to extract useful explanations. Rules extracted by TE2Rules are guaranteed to closely approximate the tree ensemble, by considering the joint interactions of multiple trees in the ensemble.

References:

TE2Rules: You can find the code: https://github.com/linkedin/TE2Rules and documentation: https://te2rules.readthedocs.io/en/latest/ here.

Disclosure: I'm one of the core developers of TE2Rules.

Upvotes: 2

smci
smci

Reputation: 33970

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.

Upvotes: 0

sashkello
sashkello

Reputation: 17871

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.

Upvotes: 4

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