Oliver,
I am well aware that joblib does the saving TO DISK, but if the model is
not saved (or stored, if you prefer that term) in the Pipeline data
structure, it doesn't help me to write that data structure to disk. My
question was whether the estimator in the Pipeline is the fitted classifier
or simply an instance of the class with no training.
On Tue, Jul 30, 2013 at 12:22 PM, <
[email protected]> wrote:
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> 1. Re: Trouble Using Cross Validation (Andreas Mueller)
> 2. Re: Finding the Features a Model Used? (Olivier Grisel)
> 3. KDTree/BallTree surprising benchmark results (Gael Varoquaux)
> 4. Why can cross validation not return multiple values? (Wifi Gi)
> 5. Re: KDTree/BallTree surprising benchmark results (Olivier Grisel)
> 6. Re: Why can cross validation not return multiple values?
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> 7. Re: KDTree/BallTree surprising benchmark results
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> ------------------------------
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> Message: 2
> Date: Tue, 30 Jul 2013 18:32:55 +0200
> From: Olivier Grisel <[email protected]>
> Subject: Re: [Scikit-learn-general] Finding the Features a Model Used?
> To: scikit-learn-general <[email protected]>
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> 2013/7/30 Wifi Gi <[email protected]>:
> > Gael,
> > Ok, thanks for letting me know.
> >
> > Oliver,
> > Do those Pipeline objects save the fitted models or do they just save the
> > steps that were taken? I can't really tell from the documentation.
>
> The Pipeline does not save anything, `joblib.dump` does. As joblib can
> pickle nested datastructures it will dump the estimators wrapped in a
> pipeline datastructure (that is the content of the step attribute).
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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> End of Scikit-learn-general Digest, Vol 42, Issue 103
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