On Tue, Apr 10, 2012 at 02:44:56PM +0200, Jaques Grobler wrote: > This paper mentions > We also show that the nonnegative garrote has the nice property that > with probability tending to one, the solution path contains an > estimate that correctly identi es the set of important variables and > is consis- tent for the coe.cients of the important variables. Such > property is valid for another popular variable selection method, > LASSO, only under restrictive con- ditions.
Yes, basically the non-negative garrote is a non-negative Lasso, if I understand it correctly. Thus if your priors are that your model is sparse, and with only positive weights, the non-negative garrote is the right estimator. Capturing garrotes with a lasso seems a bit overkill, though. G ------------------------------------------------------------------------------ Better than sec? Nothing is better than sec when it comes to monitoring Big Data applications. Try Boundary one-second resolution app monitoring today. Free. http://p.sf.net/sfu/Boundary-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
