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 class '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)
}