Like Luca mentioned, if your primary goal is to train a well-performing model
for regression or classification, you don't really have to worry much about
collinearity, e.g., if you are using regularized models. For classification via
decision trees, you probably want to reduce the number of features, or better:
use a Random Forest.
Best,
Sebastian
> On Apr 13, 2015, at 11:11 AM, Luca Puggini <lucapug...@gmail.com> wrote:
>
> It is difficult to say what you should do. In particular it depends on what
> you want to do. If you are interested only in prediction performances do not
> care about collinearity
>
>
> On Sat, Apr 11, 2015, 19:19 Ady Wahyudi Paundu <awpau...@gmail.com
> <mailto:awpau...@gmail.com>> wrote:
> Can I?
> I'm not that good in statistics, so I try it in R. Results from PCA and VIF
> were totally different
>
> ~ady
>
>
> On Sunday, April 12, 2015, Luca Puggini <lucapug...@gmail.com
> <mailto:lucapug...@gmail.com>> wrote:
> Maybe you can try pca?
>
>
> On Sat, Apr 11, 2015, 18:24 Ady Wahyudi Paundu <awpau...@gmail.com <>> wrote:
> Hi all,
>
> In scikit-learn, how to pre-processing data and remove multicollinearity?
>
> ~Ady
>
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