It only makes sense train a tree on a subset as part of an ensembl method,
and in that case you can train a set of trees by training each one on a
subset of the data (be sure to randomly choose the subset though).
It's true that ensembl methods like RandomForest don't have partial_fit,
but you cou
That explains the confusion!
Thanks, guys.
Tommy
On Sat, Oct 27, 2012 at 5:25 AM, Joseph Turian wrote:
> Gilles,
>
> I met Tommy Guy at the pydata conference today.
> If I remember correctly, Brian Eoff (I don't have his email address)
> errantly said that random forests partitions/samples the
Hey Scikit-Learn,
I've been working on some changes to the RandomForest code and I had a
few questions.
First, it looks like the function
def _partition_features(forest, n_total_features):
partitions features evenly across cores. Am I reading that correctly?
If so, does this mean that on 2 cores
I wonder what their core tree algorithms are and how fast they are. It
seems they're achieving those speeds by either
A) Significantly optimizing the tree learner
or
B) Using a smart cutoff on the size of the forest based
Tommy Guy
On Mon, Aug 27, 2012 at 10:14 AM, Jaques Grobler
wrote:
> cool l
