Dear All, I would like to use a randomForest algorithm on a dataset. The set is not particularly large/difficult to handle, but it has some missing values (both factors and numerical values). According to what I found
https://stat.ethz.ch/pipermail/r-help/2005-September/078880.html https://stat.ethz.ch/pipermail/r-help/2007-January/123117.html the randomForest package has a problem with missing data (essentially you have to resort to some "trick" to introduce them into your dataset --a median value, the most common factor, a linear interpolation etc...). Seen that I could not find a clear workaround for this (but I cannot be the only one who has in mind to do a randomForest on a less than perfect data set), can anyone help me out? I am concerned about the consequences of introducing the missing values into the data set. The cforest function in the "Party" package does not seem to have this limitation, but on the other hand the randomForest package has passed the test of time....so should I drop it in this case? Any suggestion is appreciated. Cheers Lorenzo ______________________________________________ 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.
