I am using R version 3.3.2 on windows 8. I am trying to create a decision tree using C5.0 algorithm in C50 package. Whenever I use cost matrix, the decision tree length is coming to be zero. On using similar code as well as data set, I have seen people getting results. Although, I am not absolutely certain about exact similarity of data set. Is it possible? Am I doing something wrong? I am attaching my code.
C5.0_test<-function(credit){
credit$default<-factor(credit$default, levels = c(1, 2), labels = ("non-defaulter, defaulter"))
set.seed(12345)
credit_rand <- credit[order(runif(1000)), ]
credit_train <- credit_rand[1:900, ]
credit_test <- credit_rand[901:1000, ]
credit_model <- C5.0.default(credit_train[-17], credit_train$default)
credit_pred <- predict(credit_model, credit_test)
credit_boost10 <- C5.0.default(credit_train[-17], credit_train$default, trials = 10)
credit_boost_pred10 <- predict(credit_boost10, credit_test)
error_cost <- matrix(c(0, 1, 2, 0), nrow = 2) # create a cost matrix
credit_cost <- C5.0.default(credit_train[-17], credit_train$default,
costs = error_cost) # Apply the cost matrix to the tree
credit_cost_pred <- predict(credit_cost, credit_test)
CrossTable(credit_test$default, credit_cost_pred, prop.chisq = F, prop.c = F, prop.r = F)
}