predict-workflow | R Documentation |
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.
## S3 method for class 'workflow'
predict(object, new_data, type = NULL, opts = list(), ...)
object |
A workflow that has been fit by |
new_data |
A data frame containing the new predictors to preprocess
and predict on. If using a recipe preprocessor, you should not call
|
type |
A single character value or |
opts |
A list of optional arguments to the underlying
predict function that will be used when |
... |
Additional
|
A data frame of model predictions, with as many rows as new_data
has.
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)
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