Is there papers that explain how sample weighting is used with each of
those specific classifiers ? In other words, papers explaining how each of
those classifiers is modified to support sample weighting.
2014-06-17 11:31 GMT+02:00 Arnaud Joly <[email protected]>:
> Hi,
>
> Without being exhaustive Random forest, extra trees, bagging, adaboost,
> naive bayes and several linear
> models support sample weight.
>
> Best regards,
> Arnaud
>
>
> On 17 Jun 2014, at 11:27, Mohamed-Rafik Bouguelia <
> [email protected]> wrote:
>
> Hello all,
>
> I've tried to associate weights to instances when training an SVM in
> sklearn (
> http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html
> ). Is it possible to use instance weighting with other classifiers from
> sklearn (others than the SVM) ? Basically, I know that any classifier can
> be modified to handle instances weighting, but I don't known which
> classifiers already support that in sklearn.
>
> Thanks.
>
> Rafik.
>
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--
Mohamed-Rafik BOUGUELIA
PhD Student
INRIA Nancy Grand Est - LORIA - READ Team
Nancy University - France.
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