Augment data with predictions
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.
## S3 method for class 'workflow' augment(x, new_data, ...)
x |
A workflow |
new_data |
A data frame of predictors |
... |
Arguments passed on to methods |
new_data with new prediction specific columns.
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)
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