Hi all,
From HashingVectorizer's document, it said:
- there is no way to compute the inverse transform (from feature indices to
string feature names) which can be a problem when trying to introspect
which features are most important to a model.
but i'm wondering if i can keep the mapping somewhere else to do the inverse
transform? e.g.
i can just get the indices from hashingvectorizer.transform([text]).nonzero()
and then get the
words from text or pass a dictionary to hashingvectorizer.transform to make
sure words/indices are
in consistent order.
one problem with it is there can be collisions, so different words can map to
same indices, but
i think it's quite rare, especially if only want to get the most important
feature from single document.
what do you think?
Best Regards,
--Terry------------------------------------------------------------------------------
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