I'm trying to use the caret package to do repeated k-fold cross validation with
C5.0 decision trees.
The following code generates a working C5.0 decision tree (68% accuracy on
confusion matrix):
> model <- C5.0(as.factor(OneGM) ~., data=OneT.train)
> results <- predict(object=model, newdata=OneT.test, type="class")
The caret package code gives these errors:
> train_control <- trainControl(method="repeatedcv", number=10, repeats=10)
> model <- train(as.factor(OneGM) ~., data=OneT.train,
trControl=train_control, method="C5.0")
Error in na.fail.default(list(`as.factor(OneGM)` = c(1L, 1L, 1L, 1L, :
missing values in object
> model <- train(OneGM ~., data=OneT.train, trControl=train_control,
method="C5.0")
Error in na.fail.default(list(OneGM = c(FALSE, FALSE, FALSE, FALSE, :
missing values in object
The data is loaded from a .csv file, and OneGM is either TRUE or FALSE (a text
column in the .csv).
I would like to use the one-line caret package approach above (which I've seen
used in multiple places), and I'm not looking for solutions that do cross
validation manually.
Thanks for any help.
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
