[R] Decision tree model using rpart ( classification

[aajit75]

Hi,

Thanks for the responce, code for each case is as:

c_c_factor <- 0.001  
min_obs_split <- 80

A)

fit <- rpart(segment ~., method="class", 
	   control=rpart.control(minsplit=min_obs_split, cp=c_c_factor), 
	   data=Beh_cluster_out)

B)
fit <- rpart(segment ~., method="class", 
	   control=rpart.control(minsplit=min_obs_split, cp=c_c_factor), 
	   data=profile_cluster_out)

 C)
fit <- rpart(decile ~., method="class", 
	   control=rpart.control(minsplit=min_obs_split, cp=c_c_factor), 
	   data=dtm_ip)

In A and B target variable 'segment' is from the clustering data using same
set of input variables , while in C target variable 'decile' is derived from
behavioural variables and input variables are from profile data. Number of
rows in the input table in all three cases are same.

Regards,
-Ajit


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