Hi,
I know that LARS is usually faster.
On the other side CD is often considered more robust. In particular in
situation p>>n the Lars is not able to include in the model more than n
variables. I think that the best think to do would be to include the
possibility to choice which algorithm to use and leave Lars as the default
choice.
I think that should also be included the option to use as penalty path the
lasso penalty path. This will be closer to the original paper.
I have seen that the current choice of using 'aic' or 'bic' alpha does not
work well in some situations.
Hope this could help,
Luca
>
> > I was wondering if there is any reason of why the randomized l1 algorithm
> > from the stability selection paper is implemented only using Lars Lasso
> and
> > not the coordinate descent algorithm.
> > I think than including a version of the algorithm with the coordinate
> > descent method would be very useful.
>
> because on our use case the lars was always faster. So there was no
> point supporting both.
>
> Best,
> Alex
>
------------------------------------------------------------------------------
HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
Find What Matters Most in Your Big Data with HPCC Systems
Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
Leverages Graph Analysis for Fast Processing & Easy Data Exploration
http://p.sf.net/sfu/hpccsystems
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
