Going off of my unit tests in my branch for classes= in init:
Single output:
clf = tree.DecisionTreeClassifier(classes=[1,2,3,4])
clf.fit([[10]], [0])
In [13]: clf.classes_
Out[13]: [array([0, 1, 2, 3, 4])]
Multi-output:
clf = tree.DecisionTreeClassifier(classes=[[-1,1],[0,1,2,3,4]])
y_hat = clf.fit(X_multi, y_multi).predict(T)
In [9]: clf.classes_
Out[9]: [array([-1, 1]), array([0, 1, 2, 3, 4])]
So it's as you say and I understand why. Maybe we could have a method
``supports_multi_output`` that returns a boolean so we know what shape the
classes_ are given some arbitrary clf? Or just introspect it?
Doug
On Thu, Nov 29, 2012 at 9:57 AM, Gilles Louppe <g.lou...@gmail.com> wrote:
> Hi,
>
> 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.
>
> I guess we could transform classes_ directly into that array if the
> decision tree is trained on a 1D-output, actually just like we already
> do at the prediction time.
>
> What do you think?
>
> Gilles
>
> On 29 November 2012 18:48, <amuel...@ais.uni-bonn.de> wrote:
> > the classes_ attribute is not present in all classifiers and not
> consistent,
> > as you noticed.
> > this is a known issue (see the issue tracker) and it would be great to
> > address this.
> > I am not sure about the decision trees in particular.
> >
> >
> >
> > Doug Coleman <doug.cole...@gmail.com> schrieb:
> >>
> >> Decision trees' classes are wrapped in another array for some reason. I
> >> was under the impression that I could just get ``clf.classes_`` from
> any old
> >> classifier and it would be a nice list, but I guess I'm mistaken. It
> makes
> >> it hard to write utilities...is this an oversight or a bug, or by
> design? I
> >> haven't checked other classifiers.
> >>
> >> from sklearn.tree import DecisionTreeClassifier
> >> clf1 = DecisionTreeClassifier()
> >>
> >> In [104]: clf1.classes_
> >> Out[104]: [array([1, 2, 3])]
> >>
> >>
> >> from sklearn.linear_model import SGDClassifier
> >> clf2 = SGDClassifier()
> >> clf2.fit([[1],[2],[3]], [1,2,3])
> >>
> >> In [100]: clf2.classes_
> >> Out[100]: array([1, 2, 3])
> >>
> >>
> >>
> >>
> >> Thanks,
> >> Doug
> >>
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