2012/9/10 Mathieu Blondel <[email protected]>: > Olivier: RidgeCV is based on an eigenvalue decomposition (kernel case) and > an SVD (linear case) so I think it's independent. > > Lars: That's a good idea. So we want to minimize \sum_i mu_i (w^T x_i - > y_i)^2 where mu_i is the sample weight. This should be equivalent to \sum_i > (sqrt(mu_i) w^T x_i - sqrt(mu_i) y_i)^2. So, we obtain the same result by > multiplying each y_i and x_i by sqrt(mu_i). > > Paolo: That's actually on my todo-list but I'm a bit busy lately :)
I've decided to remove RidgeClassifier from the example for now, as it's making it too hard to play with @ephes' new version of CountVectorizer. Please revert 179eabcba45bc4c9bb426fe10ec44e674f5edc33 in your PR. -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ How fast is your code? 3 out of 4 devs don\\\'t know how their code performs in production. Find out how slow your code is with AppDynamics Lite. http://ad.doubleclick.net/clk;262219672;13503038;z? http://info.appdynamics.com/FreeJavaPerformanceDownload.html _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
