Hi Luca,

If you want to find all relevant features, I would recommend using
ExtraTreesClassifier with max_features=1 and limited depth in order to
avoid this kind of bias due to estimation errors. E.g., try with
max_depth=3 to 5 or using max_leaf_nodes.

Hope this helps,
Gilles



On 19 April 2015 at 14:30, Luca Puggini <lucapug...@gmail.com> wrote:

> Hi all,
> I am using random forest and extra trees importance.
> I am wondering if there is any method to dial with correlated variables.
>
> Suppose for example the R party package.
> In page 30 of the documentation
> http://cran.r-project.org/web/packages/party/party.pdf a measure of
> 'conditional importance' is described.
>
> If I run importance with the ensamble methods in sklearn I get that
> shoesize is a relevant predictor for reading skills.
>
> Is there any way to avoid that?
>
> Thanks a lot!
>
>
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