`step_inverse()`

creates a *specification* of a recipe step that will inverse
transform the data.

## Usage

```
step_inverse(
recipe,
...,
role = NA,
offset = 0,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("inverse")
)
```

## Arguments

- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.

- ...
One or more selector functions to choose variables for this step. See

`selections()`

for more details.- role
Not used by this step since no new variables are created.

- offset
An optional value to add to the data prior to logging (to avoid

`1/0`

).- trained
A logical to indicate if the quantities for preprocessing have been estimated.

- columns
A character string of the selected variable names. This field is a placeholder and will be populated once

`prep()`

is used.- skip
A logical. Should the step be skipped when the recipe is baked by

`bake()`

? While all operations are baked when`prep()`

is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using`skip = TRUE`

as it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.

## Value

An updated version of `recipe`

with the new step added to the
sequence of any existing operations.

## Tidying

When you `tidy()`

this step, a tibble is returned with
columns `terms`

and `id`

:

- terms
character, the selectors or variables selected

- id
character, id of this step

## See also

Other individual transformation steps:
`step_BoxCox()`

,
`step_YeoJohnson()`

,
`step_bs()`

,
`step_harmonic()`

,
`step_hyperbolic()`

,
`step_invlogit()`

,
`step_log()`

,
`step_logit()`

,
`step_mutate()`

,
`step_ns()`

,
`step_percentile()`

,
`step_poly()`

,
`step_relu()`

,
`step_sqrt()`

## Examples

```
set.seed(313)
examples <- matrix(runif(40), ncol = 2)
examples <- data.frame(examples)
rec <- recipe(~ X1 + X2, data = examples)
inverse_trans <- rec %>%
step_inverse(all_numeric_predictors())
inverse_obj <- prep(inverse_trans, training = examples)
transformed_te <- bake(inverse_obj, examples)
plot(examples$X1, transformed_te$X1)
tidy(inverse_trans, number = 1)
#> # A tibble: 1 × 2
#> terms id
#> <chr> <chr>
#> 1 all_numeric_predictors() inverse_ooyvr
tidy(inverse_obj, number = 1)
#> # A tibble: 2 × 2
#> terms id
#> <chr> <chr>
#> 1 X1 inverse_ooyvr
#> 2 X2 inverse_ooyvr
```