Description Usage Arguments Value Indicator Variable Details Examples
A workflow
is a container object that aggregates information required to
fit and predict from a model. This information might be a recipe used in
preprocessing, specified through add_recipe()
, or the model specification
to fit, specified through add_model()
.
The preprocessor
and spec
arguments allow you add components to a
workflow quickly, without having to go through the add_*()
functions, such
as add_recipe()
or add_model()
. However, if you need to control any of
the optional arguments to those functions, such as the blueprint
or the
model formula
, then you should use the add_*()
functions directly
instead.
1 
preprocessor 
An optional preprocessor to add to the workflow. One of:

spec 
An optional parsnip model specification to add to the workflow.
Passed on to 
A new workflow
object.
Some modeling functions in R create indicator/dummy variables from
categorical data when you use a model formula, and some do not. When you
specify and fit a model with a workflow()
, parsnip and workflows match
and reproduce the underlying behavior of the userspecified model’s
computational engine.
In the modeldata::Sacramento data set of real
estate prices, the type
variable has three levels: "Residential"
,
"Condo"
, and "MultiFamily"
. This base workflow()
contains a
formula added via add_formula()
to predict property
price from property type, square footage, number of beds, and number of
baths:
set.seed(123) library(parsnip) library(recipes) library(workflows) library(modeldata) data("Sacramento") base_wf < workflow() %>% add_formula(price ~ type + sqft + beds + baths)
This first model does create dummy/indicator variables:
lm_spec < linear_reg() %>% set_engine("lm") base_wf %>% add_model(lm_spec) %>% fit(Sacramento)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## == Workflow [trained] ================================================
## Preprocessor: Formula
## Model: linear_reg()
##
##  Preprocessor 
## price ~ type + sqft + beds + baths
##
##  Model 
##
## Call:
## stats::lm(formula = ..y ~ ., data = data)
##
## Coefficients:
## (Intercept) typeMulti_Family typeResidential
## 32919.4 21995.8 33688.6
## sqft beds baths
## 156.2 29788.0 8730.0

There are five independent variables in the fitted model for this
OLS linear regression. With this model type and engine, the factor
predictor type
of the real estate properties was converted to two
binary predictors, typeMulti_Family
and typeResidential
. (The third
type, for condos, does not need its own column because it is the
baseline level).
This second model does not create dummy/indicator variables:
rf_spec < rand_forest() %>% set_mode("regression") %>% set_engine("ranger") base_wf %>% add_model(rf_spec) %>% fit(Sacramento)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## == Workflow [trained] ================================================
## Preprocessor: Formula
## Model: rand_forest()
##
##  Preprocessor 
## price ~ type + sqft + beds + baths
##
##  Model 
## Ranger result
##
## Call:
## ranger::ranger(x = maybe_data_frame(x), y = y, num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
##
## Type: Regression
## Number of trees: 500
## Sample size: 932
## Number of independent variables: 4
## Mtry: 2
## Target node size: 5
## Variable importance mode: none
## Splitrule: variance
## OOB prediction error (MSE): 7058847504
## R squared (OOB): 0.5894647

Note that there are four independent variables in the fitted model
for this ranger random forest. With this model type and engine,
indicator variables were not created for the type
of real estate
property being sold. Treebased models such as random forest models can
handle factor predictors directly, and don’t need any conversion to
numeric binary variables.
When you specify a model with a workflow()
and a recipe preprocessor
via add_recipe()
, the recipe controls whether dummy
variables are created or not; the recipe overrides any underlying
behavior from the model’s computational engine.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  library(parsnip)
library(recipes)
library(magrittr)
library(modeldata)
data("attrition")
model < logistic_reg() %>%
set_engine("glm")
formula < Attrition ~ BusinessTravel + YearsSinceLastPromotion + OverTime
wf_formula < workflow(formula, model)
fit(wf_formula, attrition)
recipe < recipe(Attrition ~ ., attrition) %>%
step_dummy(all_nominal(), Attrition) %>%
step_corr(all_predictors(), threshold = 0.8)
wf_recipe < workflow(recipe, model)
fit(wf_recipe, attrition)
variables < workflow_variables(
Attrition,
c(BusinessTravel, YearsSinceLastPromotion, OverTime)
)
wf_variables < workflow(variables, model)
fit(wf_variables, attrition)

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