Yuanyuan wrote:
Greetings,
I am using rpart for classification with "class" method. The test data is
the Indian diabetes data from package mlbench.
I fitted a classification tree firstly using the original data, and then
exchanged the order of Body mass and Plasma glucose which are the
strongest/important variables in the growing phase. The second tree is a
little different from the first one. The misclassification tables are
different too. I did not change the data, but why the results are so
different?
Well, at some splits the variable that comes first and yields in the
same reduction of the entropy criterion as another one might be used,
hence another result.
Uwe Ligges
Does anyone know how rpart deal with ties?
Here is the codes for running the two trees.
library(mlbench)
data(PimaIndiansDiabetes2)
mydata<-PimaIndiansDiabetes2
library(rpart)
fit2<-rpart(diabetes~., data=mydata,method="class")
plot(fit2,uniform=T,main="CART for original data")
text(fit2,use.n=T,cex=0.6)
printcp(fit2)
table(predict(fit2,type="class"),mydata$diabetes)
## misclassifcation table: rows are fitted class
neg pos
neg 437 68
pos 63 200
#Klimt(fit2,mydata)
pmydata<-data.frame(mydata[,c(1,6,3,4,5,2,7,8,9)])
fit3<-rpart(diabetes~., data=pmydata,method="class")
plot(fit3,uniform=T,main="CART after exchaging mass & glucose")
text(fit3,use.n=T,cex=0.6)
printcp(fit3)
table(predict(fit3,type="class"),pmydata$diabetes)
##after exchage the order of BODY mass and PLASMA glucose
neg pos
neg 436 64
pos 64 204
#Klimt(fit3,pmydata)
Thanks,
--------------------------------------------------------------------------------------
Yuanyuan Huang
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