`optim` a function including a caret prediction


I'm trying to use optim with a prediction of a caret object. For example, trying to find the optimal number of cylinders, power and weight of a car in order to maximise miles per galon.


# split
id <- 1:nrow(mtcars)
tr_rows <- sample(id, 22)
te_rows <- id[!id %in% tr_rows]

# features and outcome
features <- c("cyl", "hp", "wt")
outcome <- "mpg"

# data for ml
car_ml <- mtcars |> select(all_of(c(features, outcome)))

# regression
reg <- train(mpg ~ .,
      data = car_ml[tr_rows, ],
      method = "rf",
      verbose = FALSE

# minimization function
min_f <- function(x) {
  y <- predict(reg, newdata = x)

# optimization
predict(reg, newdata = car_ml[1, ]) # works
optim(par = car_ml[1, ], fn = min_f) # Error in eval(predvars, data, env): object 'cyl' not found

I can't figure out why the cyl object is not found since it is present in the x data frame.


Although someone could come with a better explanation, I answer my own question. I don't know why, but I had to transpose x in the minimization function.

# minimization function
min_f <- function(x) {
  y <- predict(reg, newdata = t(x))


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