Yuanyuan wrote:
Greetings,
I checked the Indian diabetes data again and get one tree for the data with
reordered columns and another tree for the original data. I compared these
two trees, the split points for these two trees are exactly the same but the
fitted classes are not the same for some cases. And the misclassification
errors are different too. I know how CART deal with ties --- even we are
using the same data, the subjects to the left and right would not be the
same if we just rearrange the order of covariates.
But the problem is, the fitted trees are exactly the same on the split
points. Shouldn't we get the same fitted values if the decisions are the
same at each step? Why the same structured trees have different observations
on the nodes?
Because they may use different surrogate variables. Note that your data
contain missing values that are handled by surrogates.
Best,
Uwe Ligges
The source code for running the diabetes data example and the output of
trees are attached. Your professional opinion is very much appreciated.
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$diabetes)
## misclassifcation table: rows are fitted class
neg pos
neg 437 68
pos 63 200
pmydata-data.frame(mydata[,c(1,6,3,4,5,2,7,8,9)])
fit3-rpart(diabetes~., data=pmydata,method=class)
plot(fit3,uniform=T,main=CART after exchaging mass glucose)
text(fit3,use.n=T,cex=0.6)
printcp(fit3)
table(predict(fit3,type=class),pmydata$diabetes)
##after exchage the order of BODY mass and PLASMA glucose
neg pos
neg 436 64
pos 64 204
Best,
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and provide commented, minimal, self-contained, reproducible code.
