# Caret C5.0 method takes forever to build model [closed]

I am using the caret package in R with the 'C5.0' train method. I am trying to implement kfold cross validation but it is taking too much time to build the model. How can I adjust my parameters so that it takes less time? My train data has 30,000 samples.

#My code
train_control <- trainControl(method="repeatedcv", number=10, repeats=3)

c50Grid <- expand.grid(.trials = c(1:9, (1:10)*10),
.model = c("tree", "rules"),
.winnow = c(TRUE, FALSE))

c5Fitvac <- train(y ~ .,
data = trainV,
method = "C5.0",
tuneGrid = c50Grid,
trControl = train_control,
metric = "Accuracy",
importance=TRUE,
preProc = c("center", "scale"))


## closed as off-topic by gung♦, John, Peter Flom♦Sep 19 '16 at 10:30

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• Welcome to CrossValidated. Notice I edited your post to highlight code formatting. When you say it's "taking too much time", how much is too much? Is it taking hours/days? Also, how many features you have? How many are categorical and how many categories do these have? – Firebug Sep 18 '16 at 22:35
• Notice your grid has 76 parameter combinations, you are doing 10-fold CV with 3 repeats. That's a total of 2280 evaluations. – Firebug Sep 18 '16 at 22:39
• so my last attempt took a few hours . I am trying another run and it has been running for an hour. I have 16 features, 8 are categorical with each between 2-4 categories. How do you get 76 evaluations? Sorry I know this is a silly quesiton – Umesh Nathani Sep 18 '16 at 22:54
• sorry, I meant 76 combinations – Umesh Nathani Sep 18 '16 at 23:12
• 19 entries in trials, 2 in model, 2 in winnow. – Firebug Sep 19 '16 at 2:55