Chris Lin iirc has advocated partitioning the examples then concatenation the
individual classifiers.
You could do that and then do a second pass of learning: find the 1% of
examples that are the hardest for the ensemble and learn over them.
Regardless, it will be adhoc unless you use an out
2012/9/24 Joseph Turian jos...@metaoptimize.com:
Chris Lin iirc has advocated partitioning the examples then concatenation the
individual classifiers.
You could do that and then do a second pass of learning: find the 1% of
examples that are the hardest for the ensemble and learn over them.
Thank you Olivier for these suggestions.
I'd try/test them with pleasure, but meanwhile I discovered that there
was just no way the dataset I was trying to use would ever fit in the
72GB of memory of the machine I'm using. So I just scaled it down, and
obviously this error is not happening
I think @glouppe is likely to contribute some evolution for the ensembles
of trees models once he gets back from ECML 2012 where he has a paper on
those issues.
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Hi,
I have been doing multiple experiments using a RandomForestClassifier
(trained with the parallel code option) recently, without encountering
any particular problem. However as soon as I began using a much bigger
dataset (with the exact same code), I got this threading error:
Exception in
