Training Model in Caret Using F1 Metric - r

Question

I am trying to fit a random forest model to my dataset and I would like to select the best model based off of the F1 score. I saw a post here describing the code necessary. I attempted to copy the code but I am getting the error
"Error in { : task 1 failed - "could not find function "F1_Score"
while I run the train function. (FYI the variable I am trying to predict ("pass") is a two class factor "Fail" and "Pass")
See Code Below:
library(MLmetrics)
library(caret)
library(doSNOW)
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)
}
train.control <- trainControl(method = "repeatedcv",
number = 10,
repeats = 3,
classProbs = TRUE,
summaryFunction = f1,
search = "grid")
tune.grid <- expand.grid(.mtry = seq(from = 1, to = 10, by = 1))
cl <- makeCluster(3, type = "SOCK")
registerDoSNOW(cl)
random.forest.orig <- train(pass ~ manufacturer+meter.type+premise+size+age+avg.winter+totalizer,
data = meter.train,
method = "rf",
tuneGrid = tune.grid,
metric = "F1",
weights = model_weights,
trControl = train.control)
stopCluster(cl)

Answer 1

I've rewritten the f1 function not using the MLmetrics library and it seems to work. See below for a working code to create a f1 score:
f1 <- function (data, lev = NULL, model = NULL) {
precision <- posPredValue(data$pred, data$obs, positive = "pass")
recall <- sensitivity(data$pred, data$obs, postive = "pass")
f1_val <- (2 * precision * recall) / (precision + recall)
names(f1_val) <- c("F1")
f1_val
}
train.control <- trainControl(method = "repeatedcv",
number = 10,
repeats = 3,
classProbs = TRUE,
#sampling = "smote",
summaryFunction = f1,
search = "grid")
tune.grid <- expand.grid(.mtry = seq(from = 1, to = 10, by = 1))
cl <- makeCluster(3, type = "SOCK")
registerDoSNOW(cl)
random.forest.orig <- train(pass ~ manufacturer+meter.type+premise+size+age+avg.winter+totalizer,
data = meter.train,
method = "rf",
tuneGrid = tune.grid,
metric = "F1",
trControl = train.control)
stopCluster(cl)

Answer 2

I had exactly the same error. The error also happened when I used other functions from the MLmetrics package, e.g., Precision function.
I solved it by accessing the F1_Score function using double colons ::.
f1 <- function(data, lev = NULL, model = NULL) {
f1_val <- MLmetrics::F1_Score(y_pred = data$pred,
y_true = data$obs,
positive = lev[1])
c(F1 = f1_val)
}
Using MLmetrics::F1_Score you unequivocally work with the F1_Score from the MLmetrics package.
One advantage of MLmetrics package is that its functions work with variables that have more than 2 levels.