Hi, Ningwei, I think two things could help to improve the error rate for the minority group. One is to assign bigger prior to the minority group; the other is to make the complexity parameter (cp) smaller.
Betty On 2/15/07, Liu, Ningwei <[EMAIL PROTECTED]> wrote: > > Hi, > > > > I am currently studying Decision Trees by using rpart package in R. I > artificially created a data set which includes the dependant variable > (y) and a few independent variables (x1, x2...). The dependant variable > y only comprises 0 and 1. 90% of y are 1 and 10% of y are 0. When I > apply rpart to it, there is no splitting at all. > > > > I am wondering whether this is because of the "special" distribution of > y. Since the majority of y is 1 (information in the data set is small), > rpart automatically regards it as already a single class and therefore > won't proceed any further. If this understanding is correct, what I > should do if I still want rpart to do something on this data set? > > > > > > Thanks a lot! > > > > > > Ningwei > > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ [email protected] 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.
