I think it depends by the role of sample weight during the construction of
the forest.
If I set sample_weight = 2 for one of my samples is this equivalent to
duplicate the row in the data?
On Fri, Apr 24, 2015 at 10:25 PM, Andreas Mueller <t3k...@gmail.com> wrote:
> The roc_auc will not take sample_weights into account if using
> cross_val_score.
> Thinking about it, I'm not sure if this a bug or a feature.
> Not sure if that was discussed before, I opened an issue:
> https://github.com/scikit-learn/scikit-learn/issues/4632
>
>
> On 04/24/2015 12:29 PM, Luca Puggini wrote:
>
> Dear all,
>
> I am quiet new to {0,1} classification problems.
> I have an unbalanced dataset and and I am using a RandomForestMethod on
> it.
>
> To evaluate the performances of my estimator I am using the
> cross_val_score function with the roc_auc metric.
>
> My understanding is that to deal with unbalanced problem I can pass the
> argument sample_weight to the random forest estimator.
>
> I do not understand if I should pass the sample_weight parameters also
> in this case or if this will bias the result obtained with roc_auc
>
> Is there any common way to do that? Have you any advice?
>
> Thanks a lot!
>
>
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