In the multi-output case, you have sets of labels for each of the
outputs. Say you are predicting pairs and the first element can be [-1, 1]
while and the second can be [1,2,3,4].
The classes in this case are [[-1,1], [1,2,3,4]]
For the single output case, predicting a label in [1,2,3,4] would look like
[[1,2,3,4]] and no second array since there is only one output. We are
proposing to flatten this case to match up with other non-multi-class
classifiers--it would now be just [1,2,3,4].
Doug
On Thu, Nov 29, 2012 at 1:00 PM, Lars Buitinck <l.j.buiti...@uva.nl> wrote:
> 2012/11/29 Gilles Louppe <g.lou...@gmail.com>:
> > Yes, since decision trees handle multi-output problems, classes_[i] is
> > an array containing the classes for the i-th output. Hence classes_[0]
> > is the array you are looking for when `y` is 1D.
>
> Excuse me, but I'm not sure I follow this. What does i loop over?
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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