Hi Andreas,
IMHO the only reasonable thing to do is to ignore samples for which
there is no oob estimation.
building a forest with less than 5 trees makes no sense in the first place,
so I would not worry if sklearn doesn't provide any warning for that
specific
problem (too "few" oob estimates).
Just for fun...
the probability for a sample of being without oob estimates is:
5 trees: p = 0.0067
20 trees: p = 2e-9
I stand by my suggestion: let's ignore samples without oob estimates
Paolo
On Wed, Jan 25, 2012 at 2:30 PM, Paolo Losi wrote:
> Hi Andreas,
>
> IMHO the only reasonable th
Hi everybody.
My pull request for oob estimates got merge a couple of days ago.
Now I noticed a behavior that I am not completely happy with.
If the number of estimator in the ensemble is small (say 1)
then the won't be a prediction for all of the samples.
The way it is currently implemented, there
