Hi Michael, thanks for your answer, it helped a lot already.
On 06.10.2014 11:06, Michael Eickenberg wrote: > may prefer using e.g. LassoLarsCV. I gave it a shot and indeed the coefs are available, and it worked really fine on my well designed test problems. However, with my real use case it performed poorly in comparison to LassoCV when compared to unseen data because of stability issues. Is there an intrinsic problem for LassoCV not being able to return the coefs? > Highly correlated designs generally result in a strong support > identification instability of these algorithms. It depends on what you > care about to be able to conclude if the warnings you see are relevant > or not. If it is prediction and your variables are highly correlated, > then whichever is selected, they will predict similarly. I am indeed interested in prediction and that is why I wanted to include as many predictors as possible. I removed all predictor pairs correlated to more than 0.98 (completely arbitrary) but I think this is the wrong strategy now. I'll filter some correlated predictors beforehand and try again with both LassoCV and LassoLarsCV Thanks a lot! Fabien ------------------------------------------------------------------------------ Slashdot TV. Videos for Nerds. Stuff that Matters. http://pubads.g.doubleclick.net/gampad/clk?id=160591471&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
