Hi.
I just replied to the thread above, maybe you weren't subscribed to the
ml yet.
Did you get an error when inputting a 1d X?
Which version of scikit-learn are you on?
X should really always be 2d. Unfortunately that is currently
inconsistent, and will be fixed soon.
So yes, that will be fixed, but it would be great to know the exact
behavior you encountered,
and the version.
Thanks,
Andy
On 08/18/2015 11:50 AM, Othman Soufan wrote:
Greetings Guys,
I came through the contributed implementation to multiclass.py in
Scikit-learn. I just have a suggestion for you to consider the case
when only one testing sample is passed to decision_function "Decision
function for the OneVsOneClassifier". As for the current
implementation, an undesirable output comes since n_samples
=X.shape[0] will take a number larger than one when X is only a single
list vector with some values. I may suggest you check the shape of X
before parsing it in a particular way, or update the documentation to
advise the user on a suggested way to get the prediction for one
testing sample.
In a sense, it is true to say that usually, there is a testing set of
many samples but in a specific case of mine, it was preferable to
predict sample by sample. I overcome this by using X[0:1,:] instead of
X[0,:] where X is a testing set of several samples.
Regards,
Othman Soufan
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