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<p>Mickael,</p>
<p>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.</p>
<p>Nicolas<br>
</p>
<div class="moz-cite-prefix">On 11/3/20 4:38 PM, Mick Men wrote:<br>
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Hello,
<br>
<br>
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.
<br>
<br>
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.
<br>
<br>
Ideally, I am looking for a way to build my own RRF that uses
scikit-learn API instead of modifying it.
<br>
Is it possible?
<br>
<br>
Thanks.
<br>
<br>
Mickael
<br>
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