Hi Satraijit, On Sun, Mar 25, 2012 at 3:02 PM, 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?
as far as number of estimators (trees) is concerned ... the higher the better. 100 is a reasonable default but if you are in a n << p setting it may be too low. for max features I would suggest performing hyper parameter search: 1, 2, 4, 8, .... p > also if a lot of the features are nuisance and most are noisy, are there any > recommendations for feature reduction using extra trees themselves. You could rank features by feature importance and perform recursive feature limitation (drop at each iteration 10% of feature discarding the least important) Ciao Paolo ------------------------------------------------------------------------------ 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
