* First some background: LarsLasso and Lasso are two different algorithms to solve the same problem (l1-penalized linear model).
As with all linear models, they have a 'normalize' parameter that can be turned of so that regressors are normalized. This is useful because the 'good' penalty on each weight is most likely to be proportional to the standard deviation of the corresponding regressor. It is not does via a preprocessing transform, because the coefs are automatically rescaled based on the normalization so that the linear model always holds. * Now the problem and question: In the scikit, for historical reasons, 'normalize' is True in LarsLasso and False in Lasso. This just tricked Fabian when writing some small demo code. I want to change this (warning backward compatibility breakage :$ ). I want to change Lasso to have normalize=True, because in my experience this is a sane behavior. This would imply, for consistency, changing ElasticNet to also have normalize=True. We would have to put the usual warnings. What do people think? In one sens this change can trick people and break in a subtle way the code that they are currently running. However, the current situation also breaks in subtle way people's expectation. G ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
