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 workflow
augment(x, new_data, ...)

Arguments

x

A workflow

new_data

A data frame of predictors

...

Arguments passed on to methods

Value

new_data with new prediction specific columns.

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) }