Hi scikiters, I am trying to compute lasso/lars for several values of the regularization parameter.
A clean way to do this, is like this: >>> alphas = [some long list of positive values] >>> models = linear_model.lars_path(X, y, alphas=alphas) Unfortunately, it seems to be very slow, compared to lars_path + manual interpolation on each alpha. My understanding is that it is because the returned model is an ElasticNet and, as such, cannot use lars regularization path to efficiently compute the solutions for several alphas. Am I missing something? If I need speed, should I interpolate myself? Is it worth a pull request? -- Charles-Pierre ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
