Ravi, If you don't like my questions, simply don't answer them.
-- N On 9/14/09 10:12 AM, Ravi Varadhan wrote:
Noah, It may be just me - but how does "any" of your questions on prediction modeling relate to R? It seems to me that you have been getting a lot of "free" consulting from this forum that is supposed to be a forum for help on R-related issues. Ravi. ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: rvarad...@jhmi.edu Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h tml ---------------------------------------------------------------------------- -------- -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Noah Silverman Sent: Monday, September 14, 2009 1:00 PM To: r help Subject: [R] Strange question/result about SVM Hello, I have a very unusual situation with an SVM and wanted to get the group's opinion. We developed an experiment where we train the SVM with one set of data (train data) and then test with a completely independent set of data (test data). The results were VERY good. I found and error in how we generate one of or training variables. We discovered that it was indirectly influenced by future events. Clearly that needed to be fixed. Fixing the variable immediately changed our results from good to terrible. (Not a surprise since the erroneous variable had future influence.) A friend, who knows NOTHING of statistics or math, innocently asked, "Why don't you just keep that variable since it seems to make your results so much better." The idea, while naive, led me to thinking. We can include future data in the training set, since it occurred in the past, but what to do with the test data from today? As a test, I tried simply setting the variable to the average of the value in the training data. The results were great! Now since the data is scaled, and we set the variable to the same value (constant from average of training data.) it scaled to 0. Still, great results. To summarize: Bad var in training + Bad var in testing = great results Good var in training + Good var in testing = bad results Bad var in training + Constant in testing = great results. I'm not an expert with the internals of the SVM, but clearly the bad variable is setting some kind of threshhold or intercept when defining the model. Can someone help me figure out why/how this is working? Thanks! -- N ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
