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.

## Usage

# S3 method for workflow
predict(object, new_data, type = NULL, opts = list(), ...)

## 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.

## Value

A data frame of model predictions, with as many rows as new_data has.

## Examples

library(parsnip)
library(recipes)
library(magrittr)

training <- mtcars[1:20, ]
testing <- mtcars[21:32, ]

model <- linear_reg() %>%
set_engine("lm")

workflow <- workflow() %>%

recipe <- recipe(mpg ~ cyl + disp, training) %>%
step_log(disp)

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