On Thu, Jul 12, 2012 at 10:03:41AM +0200, Andreas Müller wrote: > What do you think? I'm not sure what would be the easiest way to accomplish > this but I think it is worth investigating.
I agree. > One possible way would be to build a normal model and then have "apply_tree" > or "predict_tree" > check the regularization conditions, i.e. make them stop at a certain depth. > (or rather have separate methods that do that). The way we have been doing regularization paths in the scikit so far is to have a 'path' function that can fit the model on a full path and return either a family of models, or, given test data, a list of test errors. This path function is then called by a 'FoobarCV' object to implement parameter selection. I think that their would be value in formalizing a bit more this pattern, as it pops up everywhere. It even goes beyond this: when doing parameter selection on a model for which certain parameters have a path and others do not, there would be value to use a combination of path and grid search. Having a clear path API would help here. I don't know if that answers your question, though. Gael ------------------------------------------------------------------------------ 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
