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. 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. > ------------------------------------------------------------------------------ > ______________________________________________ [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
