Train function in caret returns error message

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I am using caret train() function to find an optimal cp value for a CART decision tree adopting as metric the F1 through a custom function. The train() function returns an error I can not understand. Perhaps the problem lies in the way I define the reproducible example?

> library(data.table)
> library(ROSE)
> data(hacide)
> train <- hacide.train
> test <- hacide.test
> numFolds = trainControl(method = "cv" , number = 10)
> cpGrid = expand.grid(.cp = seq(0.01, 0.5, 0.01))
> f1 <- function(data, lev = NULL, model = NULL) {
+   f1_val <- F1_Score(y_pred = data$pred, y_true = data$obs, positive = lev[1])
+   c(F1 = f1_val)
+ }
> set.seed(12)
> train(cls ~ ., data = train,
+              method = "rpart",
+              tuneLength = 5,
+              metric = "F1",
+              trControl = trainControl(summaryFunction = f1, 
+                                       classProbs = TRUE))
Error in train.default(x, y, weights = w, ...) : 
  At least one of the class levels is not a valid R variable name; This will cause errors when class probabilities are generated because the variables names will be converted to  X0, X1 . Please use factor levels that can be used as valid R variable names  (see ?make.names for help).
> levels(train$cls)
[1] "0" "1"
> class(train$cls)
[1] "factor"
r-caret r
1 Answers

You can try this :

levels(train$cls) <- make.names(levels(train$cls))

Then run your model this should fix your problem, Unfortunately your example is not reproducible as you missed out F1_Score function definition in your question. See if this works.

The below is working for me:

levels(train$cls) <- make.names(levels(train$cls)) 
set.seed(12)
train(cls ~ ., data = train,method = "rpart",tuneLength = 5,
                     metric = "ROC", trControl = trainControl(summaryFunction = twoClassSummary,  classProbs = TRUE))
user_18943880
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