[scikit-learn] implementing regularized random forest
Nicolas Hug
niourf at gmail.com
Tue Nov 3 11:44:00 EST 2020
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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