Cannot create tree plot using C5.0


I'm trying to fit a model using C5.0. My code is the following:

control <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
fit_train <- train(vowel ~ F1 + F2 + F3 + duration, data = train, method = "C5.0", trControl = control)

The problem is that I want to plot the tree, but instead of that, I get a plot of boosting iterations when I use the plot() function (see the figure below).

Any suggestions on how to plot the tree?

Does this work?


No, I get this error:
Error in data.frame(eval(parse(text = paste(obj$call)[xspot])), eval(parse(text = paste(obj$call)[yspot])), : arguments imply differing number of rows: 880, 220, 0

something seems wrong with the caret C50 integration ...
I think you can try an approach of getting the parameters of the best fits from the caret return, and then use that to fit a standalone c50model.

c5model <- C5.0(vowel ~ F1 + F2 + F3 + duration,
                       data = train,
                       trials = fit_train$bestTune$trials, 
                       rules = FALSE,
                       control = C5.0Control(winnow = fit_train$bestTune$winnow))

This seems to work, thanks!

note that I think you can only plot tree based C50s, not the rule type C50's, hence the rules=FALSE choice I showed

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