Why would you want to set the coefficients manually? If you want to reuse
the model, you can always save the model and load it when you want to use
it. If you want to train the model in an online setting, I think there is a
SGD api for this.
On Sat, Dec 17, 2011 at 3:28 AM, Jieyun Fu <[email protected]> wrote:
> So I read the scikit-learn package webpate:
>
>
> http://scikit-learn.sourceforge.net/dev/modules/generated/sklearn.linear_model.LogisticRegression.html
>
> I can use logistic regression to fit the data, and after I obtain an
> instance of LogisticRegression, I can use it to classify new data points.
> So far so good.
>
> Is there a way to set the coefficients of LogisticRegression() instance
> though? Because after I obtain the trained coefficients, I want to use the
> same API to classify new data points. I also don't want to keep retraining
> the data to obtain that particular instance that has the coefficients I
> want.
>
> For example, I want to do something like
>
> lg = LogisticRegression()
>
> lg.coef_ = [blah blah blah]
>
>
> ------------------------------------------------------------------------------
> Learn Windows Azure Live! Tuesday, Dec 13, 2011
> Microsoft is holding a special Learn Windows Azure training event for
> developers. It will provide a great way to learn Windows Azure and what it
> provides. You can attend the event by watching it streamed LIVE online.
> Learn more at http://p.sf.net/sfu/ms-windowsazure
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>
>
--
Best Wishes
--------------------------------------------
Meng Xinfan（蒙新泛）
Institute of Computational Linguistics
Department of Computer Science & Technology
School of Electronic Engineering & Computer Science
Peking University
Beijing, 100871
China
------------------------------------------------------------------------------
Learn Windows Azure Live! Tuesday, Dec 13, 2011
Microsoft is holding a special Learn Windows Azure training event for
developers. It will provide a great way to learn Windows Azure and what it
provides. You can attend the event by watching it streamed LIVE online.
Learn more at http://p.sf.net/sfu/ms-windowsazure
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