I tried using tidymodels and grid to tune brulee logistic regression and it gave me error that expeced "mixture" tuning to be a double. But if I do hyper_log_grid$mixture %>% class() its output is 'numeric'. Numeric implies double right? Man, I even added %>% as.double() at the input.

[1] "\033[1m\033[33mError\033[39m in \033[38;5;254m`check_double()`\033[39m:\033[22m\n\033[33m!\033[39m brulee_logistic_reg() expected 'mixture' to be a double."
[2] "\033[1m\033[33mError\033[39m in \033[38;5;254m`check_double()`\033[39m:\033[22m\n\033[33m!\033[39m brulee_logistic_reg() expected 'mixture' to be a double."
[3] "\033[1m\033[33mError\033[39m in \033[38;5;254m`check_double()`\033[39m:\033[22m\n\033[33m!\033[39m brulee_logistic_reg() expected 'mixture' to be a double."
[4] "\033[1m\033[33mError\033[39m in \033[38;5;254m`check_double()`\033[39m:\033[22m\n\033[33m!\033[39m brulee_logistic_reg() expected 'mixture' to be a double."

I didn't have the chance to apply the allow_par = FALSE, but I vaguely remember that I was still getting error with grid = 10. But don't take my words for it, since I decided to ditch the brulee logistic regression for C.50 decision tree.