As Vlad suggests, the number of topics is a hyper-parameter, and you can
optimize the value using cross-validation. Though there are other
hyper-parameter estimation methods in sklearn I think. There are also many
other closely related projects which could wrap your NMF and report back the
ideal number of topics (see on github: Spearmint, hyperopt).
Hope this helps,
- Lee
On April 29, 2015 at 3:54:49 PM, Vlad Niculae (zephy...@gmail.com) wrote:
Another thing I've seen people do is to threshold based on the
difference between the scores of the best and second best topics.
(Only take documents with a clear winning topic.) For estimating the
number of topics, you can use cross-validation.
Vlad
On Wed, Apr 29, 2015 at 12:42 AM, Joel Nothman <joel.noth...@gmail.com> wrote:
> mask with np.max(..., axis=1) > threshold
>
> On 29 April 2015 at 14:35, C K Kashyap <ckkash...@gmail.com> wrote:
>> How can I include a document against it's highest ranking topic only if it
>> crosses a threshold?
>>
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