[pymvpa] How to evaluate the goodness of classification for an unlabelled example?
robbenson18 at gmail.com
Fri Jan 18 14:16:47 UTC 2013
I have a question that do not strictly concern to PyMVPA strictly.
I trained a classifier to discriminate two classes (e.g. bananas and
apples), using SVM, cross-validation etc. then I would like to try it with
some "unlabelled" fruits, could be, bananas and apples but also melon,
lemon, strawberries. If I try to classify a melon, the label assigned by
the classifier could be banana. How can I establish a probability level for
this fruit? I mean, if I use SVM distance from the hyperplane, the melon
could be distant from bananas and further from apples (hyperspaces) and
thus in my opinion this is not a good index for that. I would like to have
an index that tries to tell me that is a banana only with higher
probability than apples: p(bananas) = 0.3 p(apple) = 0.1 for example.
Hope it is an xhaustive and an answerable question!
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