Hi all, I have a large data set and want to immediately build a 'blind' model without first examining the data. Now it appears in the data there are a lot of fields that are constant or all missing values - which prevents the model from being built.
Can someone point me the right direction as to how I can automatically purge my data file of these useless fields. Thanks in advance, pdb train <- read.csv("TrainingData.csv") library(gbm) i.gbm<-gbm(TargetVariable ~ . ,data=train,distribution="bernoulli..... 1: In gbm.fit(x, y, offset = offset, distribution = distribution, ... : variable 5: var1 has no variation. -- View this message in context: http://r.789695.n4.nabble.com/eliminating-constant-variables-tp2284831p2284831.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.