Hi Michael, > I agree it would be nice to see a multinomial logistic regreesion in the > scikit :). Any plans for it?
not on my side but this question comes so often that it might become a priority for the project. > Is your warning about interpreting the weights just because in a penalized > model they're not best estimators? my warning is that in some cases there is a mismatch between the weights obtained with cross-validation (optimization of prediction score) and the weights obtained with the alpha (reg. parameter) that optimizes the estimation of the weights. It might be counter intuitive but it's particularly the case when n_features >> n_samples e.g. when you work with full brain data and a sparse solver. > Mostly I just want to take a look to see > in general what's happening -- e.g. does it look like there is some amount > of clustering into blobs, or are my weights mixed more or less randomly (I'm > using L2 regularization, so they aren't sparse). Also this isn't full brain > data, just restricted within a few ROIs (~500 features per model). To the > extent that I might do inference on them, it would be taking each subject's > weights to a group level random-effects test against 0. But mostly I'm just > trying to get a feel for what's driving my classification and don't care too > much about the exact values of the weights. I figured I might as well take > advantage of PySurfer's ability to draw things in lots of pretty colors :) as long as you work with ROIs and you use the weights just to get an idea of what's happening it should be fine. It's true that if you see some structure in the weights although you did not impose it, it's probably a good sign that your data contain some info. Alex ------------------------------------------------------------------------------ Keep Your Developer Skills Current with LearnDevNow! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-d2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
