On 04/25/2015 09:18 AM, Luca Puggini wrote:
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?
During fitting, yes, during evaluation currently not.
On Fri, Apr 24, 2015 at 10:25 PM, Andreas Mueller <t3k...@gmail.com
<mailto: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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