No. I think cross validation is the better way to evaluate classifiers. The diagnostics of random forests are interesting, but not critical.
On Sat, Oct 8, 2011 at 9:49 PM, deneche abdelhakim <[email protected]>wrote: > While reviewing Decision Forest code, I noticed that computing the "out of > bag" error (OOB) of the forest while training it made the implementation > really messy. I made a lot of assumptions about the way Hadoop works > internally (especially the way it splits the data), this proven many times > to be buggy because with each new version of Hadoop I hade to "tweak" the > code to make it run. > > So I am asking the users and developers alike: is computing the OOB really > necessary ? if yes, I will spend the time to figure out a better way to > compute it, but if no I will just get rid of it for now and leave a JIRA > issue about getting it back again if someone actually need it. >
