If you set sample_weight[i] = 2, for the i-th samples. It will
consider that this sample has to be accounted twice in the tree
growing procedure (impurity computation, leaf labelling, …).
Best regards,
Arnaud
> On 26 Apr 2015, at 16:00, Luca Puggini <lucapug...@gmail.com> wrote:
>
> Ok thanks a lot, a last question.
>
> What is the role of sample_weight If I use ExtraTreesClassifier with
> bootstrap=False (this is the default)?
> Are they used during the splitting process?
>
> On Sat, Apr 25, 2015 at 10:04 PM, Andy <t3k...@gmail.com
> <mailto:t3k...@gmail.com>> wrote:
> 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
>> <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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