On Fri, May 18, 2012 at 6:39 AM, Ian Goodfellow <[email protected]>wrote:
> If the class label vector passed to the "fit" method of
> OneVsRestClassifier has shape (n,1) instead of shape (n,),
> then really weird results happen. In this example script here, when
> the fit SVM is asked to predict the labels of
> 1000 examples, the resulting prediction vector has shape (45000,1),
> not (1000,) as expected.
>
Thanks for reporting. I could reproduce the problem. So the "issue" seems
to be in LabelBinarizer. To fix this, I suggest that we just raise an error
in LabelBinarizer when the input to fit has 2d shape. Personaly, I'm not in
favor of converting an array of shape (n_samples, 1) to (n_samples, )
behind the scenes. The reason is because those shapes have different
semantics. If we start accepting arrays of shape (n_samples, 1), I would
expect arrays of shape (n_samples, n_tasks) to work too (just like
multivariate regression).
PR welcome :)
Mathieu
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