We don't have a PMML interface yet [1] - so you need to write custom code
to extract internal state each individual classifier.
What do you mean by performance critical (<1ms, <<1ms)? Do you make
predictions per sample or can you buffer samples and make predictions for
batches?
In general, what kills performance is the overhead of python function calls
- its usually way larger than the actual prediction (which usually happens
in C-land).
[1] http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language
2013/9/23 Fred Baba <fred.b...@gmail.com>
> I'd like to use classifiers trained via sklearn in a real-time
> application, performance critical application. How do I access the internal
> representation of trained classifiers?
>
> For linear classifiers/regressions, I can simply store the coefficients
> and generate the linear combination myself. For tree regressions, I can use
> sklearn.tree.export_graphviz. Ideally there would be an export facility for
> all classifiers (particularly for examining the structure of generated
> models). Is there a general solution way to do this?
>
>
>
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Peter Prettenhofer
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