Hi Meghana.
I'm -1 on adding decision jungles.
The main benefit seems to be faster predictions, something sklearn is
not really focusing on.
Also, it is a very recent publication, and I don't think it is widely used.
I think Mondrian Forests
http://arxiv.org/pdf/1406.2673v2
https://github.com/balajiln/mondrianforest
would be much more interesting, but I also think they are not
established enough yet
(and the current implementation is python with MIT licence :)
Cheers,
Andy
On 03/12/2015 09:42 AM, meghana madhyastha wrote:
Hi All,
I am an undergraduate student who has worked on machine learning
projects using sklearn. I recently came across a paper published by a
few researchers Microsoft Research about decision jungles. To put it
briefly, randomized decision trees face a problem that given a lot of
data, the number of nodes in the decision trees will grow in depth.
Decision Jungles addresses this problem by introducing the idea of
ensembles of DAGs. I thought that this can be implemented in sklearn
and I personally think its useful. I might be applying for GSOC 15.
-Meghana
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