> From: Edgar Acuna [mailto:[EMAIL PROTECTED] > > Dear Andy, > Thanks for your quick answer. I increased the number of trees and the > outlyingness measure got more stable. But still I do not know if I am > working with the raw measure or with the normalized measure mentioned > in the Breiman's Wald lecture. The normalized measure nout is > > nout=(nout-med)/mean(abs(nout-med)) > where med is the median of the class containing the case correponding > to nout.
Looking at the Fortran subroutine `locateout' in rfsub.f, yes, they are normalized. (That part of the code is not changed from Breiman & Cutler's original.) Andy > Best regards > Edgar Acuna > > On Sun, 18 Apr 2004, Liaw, Andy wrote: > > > The thing to do is probably: > > > > 1. Use fairly large number of trees (e.g., 1000). > > 2. Run a few times and average the results. > > > > The reason for the instability is sort of two fold: > > > > 1. The random forest algorithm itself is based on > randomization. That's why > > it's probably a good idea to have 500-1000 trees to get more stable > > proximity measures (of which the outlying measures are based on). > > > > 2. If you are running randomForest in unsupervised mode > (i.e., not giving it > > the class labels), then the program treats the data as > "class 1", creates a > > synthetic "class 2", and run the classification algorithm to get the > > proximity measures. You probably need to run the algorithm > a few times so > > that the result will be based on several simulated data, > instead of just > > one. > > > > HTH, > > Andy > > > > > From: Edgar Acuna > > > > > > Hello, > > > Does anybody know if the outscale option of randomForest > yields the > > > standarized version of the outlier measure for each case? or > > > the results > > > are only the raw values. Also I have notice that this > measure presents > > > very high variability. I mean if I repeat the experiment I am > > > getting very > > > different values for this measure and it is hard to flag > the outliers. > > > This does not happen with two other criteria than I am > using: LOF and > > > Bay's Orca. I am getting several cases that can be considered > > > as outliers > > > with both approaches. > > > I run my experiments using Bupa and Diabetes available at > > > UCI Machine database repository. > > > > > > Thanks in advance for any response. > > > > > > ______________________________________________ > > > [EMAIL PROTECTED] mailing list > > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide! > > > http://www.R-project.org/posting-guide.html > > > > > > > > > > > > > -------------------------------------------------------------- > ---------------- > > Notice: This e-mail message, together with any > attachments, contains > > information of Merck & Co., Inc. (One Merck Drive, > Whitehouse Station, New > > Jersey, USA 08889), and/or its affiliates (which may be > known outside the > > United States as Merck Frosst, Merck Sharp & Dohme or MSD > and in Japan as > > Banyu) that may be confidential, proprietary copyrighted > and/or legally > > privileged. It is intended solely for the use of the > individual or entity > > named on this message. If you are not the intended > recipient, and have > > received this message in error, please notify us > immediately by reply e-mail > > and then delete it from your system. > > > -------------------------------------------------------------- > ---------------- > > > > > ------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
