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> 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>
> 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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