Hi Satrajit, Adding more trees should never hurt accuracy. The more, the better.
Since you have a lot of irrelevant features, I'll advise to increase max_features in order to capture the relevant features when computing the random splits. Otherwise, your trees will indeed fit on noise. Best, Gilles On 25 March 2012 15:02, Satrajit Ghosh <[email protected]> wrote: > hi giles, > > when dealing with skinny matrices of the type few samples x lots of > features what are the recommendations for extra trees in terms of max > features and number of estimators? > > also if a lot of the features are nuisance and most are noisy, are there any > recommendations for feature reduction using extra trees themselves. > > time of training or testing is not an important issue. (yes, faster is > better, but we are more interested in feature importance related to > accuracy). > > cheers, > > satra > > ------------------------------------------------------------------------------ > 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 > ------------------------------------------------------------------------------ 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
