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Practical Predictive Analytics by Ralph Winters

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The ctree algorithm

The ctree function gives some advantages over rpart in that the results produced can be a bit more intuitive. Rather than optimizing a result node based purely upon the purity of the resultant node, ctree uses statistical hypothesis testing to determine if the results of the split are statistically significant. It uses chi-square test statistics to test the association, only keeping the associations that are significant, and thus removing bias due to a large number of categories. So, while accuracy may suffer in some cases, benefit is gained by having the results be more explanatory:

install.packages("partykit") 
library(partykit) 
y2 <- ctree(Life.Exp ~ .,data=x) 
y2 
plot(y2) 
 

As you can see from the following plot, ctree ...

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