Le 25 mars 2012 17:09, Peter Prettenhofer <[email protected]> a écrit : > 2012/3/25 Olivier Grisel <[email protected]>: >> Le 25 mars 2012 12:44, Peter Prettenhofer >> <[email protected]> a écrit : >>> Olivier, >>> >>> In my experience GBRT usually requires more base learners than random >>> forests to get the same level of accuracy. I hardly use less than 100. >>> Regarding the poor performance of GBRT on the olivetti dataset: >>> multi-class GBRT fits ``k`` trees at each stage, thus, if you have >>> ``n_estimators`` this means you have to grow ``k * n_estimators`` >>> trees in total (4000 trees is quite a lot :-) ). Personally, I haven't >>> used multi-class GBRT much (part of the reason is that GBM does not >>> support it) - I know that the learning to rank folks use multi-class >>> GBRT for ordinal scaled output values (e.g. "not-relevant", >>> "relevant", "highly relevant") but these involve usually less than 5 >>> classes. >> >> Interesting I think this kind of practical considerations should be >> added to the docs. > > Absolutely - I'll add them immediately.
Great, thanks. Please use `n_classes` instead of `k` in the docstrings or narrative doc. And thanks for the other comments and references. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
