Reversing normalized values on a partial dependency plot in R

I am trying to explain my MARS model (for classification). I created a partial dependency plot based on my model:https://imgur.com/39hdHEp

As you can see I have monthly income feature on the x-axis and customer churn probability on the y-axis. Before I train my model, I centered and scaled monthlyIncome feature on the preProcess step. But now in this plot, it doesn't make any sense. so my question is:

• How can I create this plot with real monthly income values instead of the centered and scaled version?

I am using R. On preprocessing step I used "recipe" package:

blueprint <- recipe(churn ~ ., data = churn_train) %>%
  step_nzv(all_nominal())  %>%
  step_corr(all_numeric(), -all_outcomes(), threshold = .8) %>%
  step_BoxCox(all_numeric(), -all_outcomes()) %>%
  step_scale(all_numeric(), -all_outcomes()) %>%
  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)

and I created partial dependency plot by using "pdp" package:

partial(best_mars_model, pred.var = "monthlyIncome", prob = TRUE) %>% autoplot()

Thanks for help