Hi there, I am looking into how to implement multi-label
text classification.
Generally, am training models just as normal multi-class classification, but
for predication/test, I want to make it multi-label, i.e. getting more than
one labels for predication on a given test item.
The easiest way would be implementing a battery of one-vs-all classifiers,
which actually as I understand, is what happens under the hood when one uses
most of the classification methods for multi-class.
However, what I can not figure out is that how can I manipulate the process
of prediction in order to generate multi-label rather than only one label as
the prediction result.
Are there any provided APIs to do the multi-label classification, or I need
to build the multi-label classification from the ground by building the set
of classifiers by myself?
Thanks a lot. :)
A final off-topic question is, as I noticed this mailing list is more for
developing coordination, am I suppose to post question like this to
stackoverflow or some other places? Also, communications in mailing list
seems to be not SEO optimized so that others hardly can search and get
to the contents in the mailing list. This may be a waste of resources.
------------------------------------------------------------------------------
The demand for IT networking professionals continues to grow, and the
demand for specialized networking skills is growing even more rapidly.
Take a complimentary Learning@Cisco Self-Assessment and learn
about Cisco certifications, training, and career opportunities.
http://p.sf.net/sfu/cisco-dev2dev
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
