This is a `generics::augment()`

method for a workflow that calls
`augment()`

on the underlying parsnip model with `new_data`

.

`x`

must be a trained workflow, resulting in fitted parsnip model to
`augment()`

with.

`new_data`

will be preprocessed using the preprocessor in the workflow,
and that preprocessed data will be used to generate predictions. The
final result will contain the original `new_data`

with new columns containing
the prediction information.

## Usage

```
# S3 method for workflow
augment(x, new_data, eval_time = NULL, ...)
```

## Arguments

- x
A workflow

- new_data
A data frame of predictors

- eval_time
For censored regression models, a vector of time points at which the survival probability is estimated. See

`parsnip::augment.model_fit()`

for more details.- ...
Arguments passed on to methods

## Examples

```
if (rlang::is_installed("broom")) {
library(parsnip)
library(magrittr)
library(modeldata)
data("attrition")
model <- logistic_reg() %>%
set_engine("glm")
wf <- workflow() %>%
add_model(model) %>%
add_formula(
Attrition ~ BusinessTravel + YearsSinceLastPromotion + OverTime
)
wf_fit <- fit(wf, attrition)
augment(wf_fit, attrition)
}
```