2013/1/23 Andreas Mueller <amuel...@ais.uni-bonn.de>:
> Am 23.01.2013 16:47, schrieb Philipp Singer:
>> That's what I originally thought, but then I tried it with just using
>> LinearSVC and it magically worked for my sample dataset, really
>> interesting. I think it is working now properly.
> I'm pretty sure it shouldn't.
Nope, it shouldn't.
>> What I am asking myself is how exactly the decision is made for the
>> multilabel prediction. Is there some way of influencing it? For example
>> sometimes it predicts zero classes and sometimes several.
> OneVsRestClassifier does - surprise - one-vs-rest classification.
> This means there is a binary classifier pre class. If none of these
> predict class presence, no class is predicted...
> You could try using different thresholds on the decision_function,
> if you want more predictions or something...
More in detail: OneVsRestClassifier exports an object called
label_binarizer_, which is used to transform decision function values
D back to class labels. By default, it picks all the classes for which
D > 0, but its threshold argument can be used to change that.
So, if clf is an OvR classifier and
D = clf.decision_function(x)
for a *single sample* x contains no positive values, then
# untested, may contain mistakes
clf.label_binarizer_.inverse_transform(D, threshold=(D.max() + epsilon))
will predict at least one class label for x, namely the one with the
highest value according to the decision_function. The epsilon is
needed because inverse_transform compares values using >, not >=; set
it to a small value. Doing this for batches of samples is a bit more
involved.
Of course, you can set the threshold to any value. Whether any of this
makes sense depends on your problem.
[I used to be opposed to exporting the LabelBinarizer object on OvR
estimators, but I guess I should give up the struggle now -- this is
actually useful.]
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
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