Quick SVM question for scikit-learn. When you train an SVM, it's something like
from sklearn import svm s = svm.SVC() s.fit(training_data, labels)
Is there any way for labels
to be a list of a non-numeric type? For instance, if I want to classify vectors as 'cat' or 'dog,' without having to have some kind of external lookup table that encodes 'cat' and 'dog' into 1's and 2's. When I try to just pass a list of strings, I get ...
ValueError: invalid literal for float(): cat
So, it doesn't look like just shoving strings in labels
will work. Any ideas?
Passing strings as classes directly is on my todo, but it is not supported in the SVMs yet. For the moment, we have the LabelEncoder that can do the book keeping for you.
[edit]This should work now out of the box[/edit]
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