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 wrote:
>
> Ok thanks a lot, a last question
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 wrote:
> On 04/25/2015 09:18 AM, Luca Puggini wrote:
>
> I think it
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 Fr
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 wrote:
> The roc_auc will not take sample_weights into a
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 wrot
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 p
On Wednesday, February 11, 2015, Michael Eickenberg <
michael.eickenb...@gmail.com> wrote:
>
>
> On Wednesday, February 11, 2015, Carlos Pita > wrote:
>
>> Hi all,
>>
>> I'm trying to port to sklearn some R code that does WLS and I noticed
>> that the fit method for some classes will accept a sam
On Wednesday, February 11, 2015, Carlos Pita
wrote:
> Hi all,
>
> I'm trying to port to sklearn some R code that does WLS and I noticed
> that the fit method for some classes will accept a sample_weight
> parameter (v.g. SGDRegressor) while for other classes it won't (v.g.
> LinearRegression). Is
Hi all,
I'm trying to port to sklearn some R code that does WLS and I noticed
that the fit method for some classes will accept a sample_weight
parameter (v.g. SGDRegressor) while for other classes it won't (v.g.
LinearRegression). Is this just inconsistent or has it a rationale
behind? Maybe in so
