Hi all,
I am very new to R (only two days of studies). I know a little bit of
statistical learning and looking for an implementation of CART and random
forest and therefore I am now studying R.
I tested with rpart and randomForest package, they are quite good.
However, I need a classification tree and for each training data, there is
different loss. (i.e. the loss function is not purely 0-1, say, c_i > 0 for
each training data)
I read the document of randomForest and found that there are no such
parameter, and then I move to rpart package to check if I can do it and
perform the bagging process by myself.
I found that there is a "weights" parameter for rpart. Is it the one that
I can use and it will ultimately modify the Gini / Cross-entropy
calculation during the learning process?
If it is yes, and I want to do the bagging process by myself without
referencing to randomForest package, is there any meta-algorithm package
available for this purpose?
Thank you very much.
Best regards,
WONG Hang.
P.S. I have tested party package also. But it is not quite suitable for me
and I face some errors on it.
[[alternative HTML version deleted]]
______________________________________________
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.
