Mickael,
You probably don't need to ship an entire fork, but all the tree
internals that you are using (splitter etc.) are part of a private API
so yes, you would need to duplicate these into your own implementation.
Nicolas
On 11/3/20 4:38 PM, Mick Men wrote:
Hello,
I am trying to implement my own regularized random forest (RRF) which
grows trees in series and selects new features only if they are better
than the features used in previous splits.
This is for a research project and I will need to ship the code with
the publication. So far I have a working proof of concept where I
modified the scikit-learn forest, tree, and splitter modules. But this
mean that I need to ship my fork version of scikit-learn.
Ideally, I am looking for a way to build my own RRF that uses
scikit-learn API instead of modifying it.
Is it possible?
Thanks.
Mickael
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