This is the `predict()`

method for a fit workflow object. The nice thing
about predicting from a workflow is that it will:

Preprocess

`new_data`

using the preprocessing method specified when the workflow was created and fit. This is accomplished using`hardhat::forge()`

, which will apply any formula preprocessing or call`recipes::bake()`

if a recipe was supplied.Call

`parsnip::predict.model_fit()`

for you using the underlying fit parsnip model.

## Arguments

- object
A workflow that has been fit by

`fit.workflow()`

- new_data
A data frame containing the new predictors to preprocess and predict on

- type
A single character value or

`NULL`

. Possible values are`"numeric"`

,`"class"`

,`"prob"`

,`"conf_int"`

,`"pred_int"`

,`"quantile"`

,`"time"`

,`"hazard"`

,`"survival"`

, or`"raw"`

. When`NULL`

,`predict()`

will choose an appropriate value based on the model's mode.- opts
A list of optional arguments to the underlying predict function that will be used when

`type = "raw"`

. The list should not include options for the model object or the new data being predicted.- ...
Additional

`parsnip`

-related options, depending on the value of`type`

. Arguments to the underlying model's prediction function cannot be passed here (use the`opts`

argument instead). Possible arguments are:`interval`

: for`type`

equal to`"survival"`

or`"quantile"`

, should interval estimates be added, if available? Options are`"none"`

and`"confidence"`

.`level`

: for`type`

equal to`"conf_int"`

,`"pred_int"`

, or`"survival"`

, this is the parameter for the tail area of the intervals (e.g. confidence level for confidence intervals). Default value is`0.95`

.`std_error`

: for`type`

equal to`"conf_int"`

or`"pred_int"`

, add the standard error of fit or prediction (on the scale of the linear predictors). Default value is`FALSE`

.`quantile`

: for`type`

equal to`quantile`

, the quantiles of the distribution. Default is`(1:9)/10`

.`time`

: for`type`

equal to`"survival"`

or`"hazard"`

, the time points at which the survival probability or hazard is estimated.

## Examples

```
library(parsnip)
library(recipes)
library(magrittr)
training <- mtcars[1:20, ]
testing <- mtcars[21:32, ]
model <- linear_reg() %>%
set_engine("lm")
workflow <- workflow() %>%
add_model(model)
recipe <- recipe(mpg ~ cyl + disp, training) %>%
step_log(disp)
workflow <- add_recipe(workflow, recipe)
fit_workflow <- fit(workflow, training)
# This will automatically `bake()` the recipe on `testing`,
# applying the log step to `disp`, and then fit the regression.
predict(fit_workflow, testing)
#> # A tibble: 12 × 1
#> .pred
#> <dbl>
#> 1 25.4
#> 2 15.4
#> 3 15.8
#> 4 14.4
#> 5 13.2
#> 6 29.4
#> 7 25.4
#> 8 27.6
#> 9 14.4
#> 10 23.2
#> 11 15.9
#> 12 25.3
```