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HELP
3 messages · Orla Carey, Bert Gunter, Weidong Gu
Your post is unacceptable (imho, of course). Read the bottom of this message (re: "posting guide") and re-post properly. -- Bert
On Thu, Jul 26, 2012 at 9:30 AM, Orla Carey <careyo at tcd.ie> wrote:
Hello,
I am running classification trees fro the purpose of predicting dividends.
I have training and test data sets, but am running to issues when
evaluating the prediction accuracy of the tree as it isn't a simple
'predict' formulation as the determinant variable is not a simple set of
classifying inputs. I think there is some RMSE method to use but can't
uncover how to do this. Any help would be greatly appreciated.
Orla
[[alternative HTML version deleted]]
______________________________________________ R-help at 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.
Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
Hi, You can try predict.rpart in rpart package for prediction on a test data. ?predict.rpart Weidong
On Thu, Jul 26, 2012 at 12:30 PM, Orla Carey <careyo at tcd.ie> wrote:
Hello,
I am running classification trees fro the purpose of predicting dividends.
I have training and test data sets, but am running to issues when
evaluating the prediction accuracy of the tree as it isn't a simple
'predict' formulation as the determinant variable is not a simple set of
classifying inputs. I think there is some RMSE method to use but can't
uncover how to do this. Any help would be greatly appreciated.
Orla
[[alternative HTML version deleted]]
______________________________________________ R-help at 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.