Hi,

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.

```
library("caret")
library("tidyverse")
data("mtcars")
# 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)
return(-y)
}
# 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.

Thanks!