The shape of X after encoding is (32769, 16600). Seems as if that is too
big to be converted into a dense matrix. Can Random forest handle this
amount of features?
On Thu, Jun 20, 2013 at 7:31 PM, Olivier Grisel <olivier.gri...@ensta.org>wrote:
> 2013/6/20 Lars Buitinck <l.j.buiti...@uva.nl>:
> > 2013/6/20 Olivier Grisel <olivier.gri...@ensta.org>:
> >>> Actually twice as much, even on a 32-bit platform (float size is
> >>> always 64 bits).
> >>
> >> The decision tree code always uses 32 bits floats:
> >>
> >>
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_tree.pyx#L38
> >>
> >> but you have to cast your data to `dtype=np.float32` in fortran layout
> >> ahead of time to avoid the memory copy.
> >
> > OneHot produces np.float, though, which is float64.
>
> Alright but you could convert it to np.float32 before calling toarray.
> But anyway this kind of sparsity level is unsuitable for random
> forests anyways I think.
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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