Thank you Andy.
It seems like this can be the reason for the confusion.
I never thought that there can be this kind of catches for using tune.* functions.
For the record, I actually emailed to Dr. Friedrich Leisch the author of this library.
When I get some reply, I will post it also.
Regards, TH
On Jul 12, 2004, at 6:40 PM, Liaw, Andy wrote:
Looking at the body of tune(), it has:
... repeat.errors[reps] <- if (is.factor(true.y)) 1 - classAgreement(table(pred, true.y)) else crossprod(pred - true.y)/length(pred) ...
where classAgreement() is a function defined inside tune() that computes the
fraction of correctly predicted cases. So it looks like tune() and friends
are returning error rates as fractions, not percentages.
You're right that the fraction shouldn't be larger than 1. Did you make
sure that tune() sees the data as classification, not regression (i.e., did
you make sure that the class labels given to tune.*() are factor)?
HTH, Andy
Tae-Hoon Chung, Ph.D
Post-doctoral Research Fellow Molecular Diagnostics and Target Validation Division Translational Genomics Research Institute 1275 W Washington St, Tempe AZ 85281 USA Phone: 602-343-8724
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