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(), or a tailor used in postprocessing,
specified through add_tailor().
The preprocessor and spec arguments allow you to 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.
Arguments
- preprocessor
An optional preprocessor to add to the workflow. One of:
A formula, passed on to
add_formula().A recipe, passed on to
add_recipe().A
workflow_variables()object, passed on toadd_variables().
- spec
An optional parsnip model specification to add to the workflow. Passed on to
add_model().- postprocessor
An optional
tailor::tailor()defining post-processing steps to add to the workflow. Passed on toadd_tailor().
Indicator Variable Details
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 user-specified model’s
computational engine.
Formula Preprocessor
In the modeldata::Sacramento data set of real
estate prices, the type variable has three levels: "Residential",
"Condo", and "Multi-Family". 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)## == 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 8730.0There 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)## == 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): 0.5894647Note 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. Tree-based models such as random forest models can
handle factor predictors directly, and don’t need any conversion to
numeric binary variables.
Recipe Preprocessor
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.
Examples
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)
#> ══ Workflow [trained] ════════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: logistic_reg()
#>
#> ── Preprocessor ──────────────────────────────────────────────────────────
#> Attrition ~ BusinessTravel + YearsSinceLastPromotion + OverTime
#>
#> ── Model ─────────────────────────────────────────────────────────────────
#>
#> Call: stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)
#>
#> Coefficients:
#> (Intercept) BusinessTravelTravel_Frequently
#> -2.82571 1.29473
#> BusinessTravelTravel_Rarely YearsSinceLastPromotion
#> 0.64727 -0.03092
#> OverTimeYes
#> 1.31904
#>
#> Degrees of Freedom: 1469 Total (i.e. Null); 1465 Residual
#> Null Deviance: 1299
#> Residual Deviance: 1194 AIC: 1204
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)
#> ══ Workflow [trained] ════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: logistic_reg()
#>
#> ── Preprocessor ──────────────────────────────────────────────────────────
#> 2 Recipe Steps
#>
#> • step_dummy()
#> • step_corr()
#>
#> ── Model ─────────────────────────────────────────────────────────────────
#>
#> Call: stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)
#>
#> Coefficients:
#> (Intercept) Age
#> -2.535e+00 -3.131e-02
#> DailyRate DistanceFromHome
#> -3.126e-04 4.927e-02
#> HourlyRate MonthlyIncome
#> 2.762e-03 1.127e-06
#> MonthlyRate NumCompaniesWorked
#> 1.663e-06 1.956e-01
#> PercentSalaryHike StockOptionLevel
#> -2.495e-02 -1.968e-01
#> TotalWorkingYears TrainingTimesLastYear
#> -6.820e-02 -1.863e-01
#> YearsAtCompany YearsInCurrentRole
#> 8.916e-02 -1.371e-01
#> YearsSinceLastPromotion YearsWithCurrManager
#> 1.849e-01 -1.516e-01
#> BusinessTravel_Travel_Frequently BusinessTravel_Travel_Rarely
#> 1.940e+00 1.080e+00
#> Education_1 Education_2
#> -1.391e-01 -2.753e-01
#> Education_3 Education_4
#> -7.324e-02 3.858e-02
#> EducationField_Life_Sciences EducationField_Marketing
#> -6.939e-01 -2.212e-01
#> EducationField_Medical EducationField_Other
#> -7.210e-01 -6.755e-01
#> EducationField_Technical_Degree EnvironmentSatisfaction_1
#> 2.936e-01 -9.501e-01
#> EnvironmentSatisfaction_2 EnvironmentSatisfaction_3
#> 4.383e-01 -2.491e-01
#> Gender_Male JobInvolvement_1
#> 4.243e-01 -1.474e+00
#> JobInvolvement_2 JobInvolvement_3
#> 2.297e-01 -2.855e-01
#> JobRole_Human_Resources JobRole_Laboratory_Technician
#> 1.441e+00 1.549e+00
#> JobRole_Manager JobRole_Manufacturing_Director
#> 1.900e-01 3.726e-01
#> JobRole_Research_Director JobRole_Research_Scientist
#> -9.581e-01 6.055e-01
#> JobRole_Sales_Executive JobRole_Sales_Representative
#> 1.056e+00 2.149e+00
#> JobSatisfaction_1 JobSatisfaction_2
#> -9.446e-01 -8.929e-03
#> JobSatisfaction_3 MaritalStatus_Married
#> -2.860e-01 3.135e-01
#>
#> ...
#> and 14 more lines.
variables <- workflow_variables(
Attrition,
c(BusinessTravel, YearsSinceLastPromotion, OverTime)
)
wf_variables <- workflow(variables, model)
fit(wf_variables, attrition)
#> ══ Workflow [trained] ════════════════════════════════════════════════════
#> Preprocessor: Variables
#> Model: logistic_reg()
#>
#> ── Preprocessor ──────────────────────────────────────────────────────────
#> Outcomes: Attrition
#> Predictors: c(BusinessTravel, YearsSinceLastPromotion, OverTime)
#>
#> ── Model ─────────────────────────────────────────────────────────────────
#>
#> Call: stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)
#>
#> Coefficients:
#> (Intercept) BusinessTravelTravel_Frequently
#> -2.82571 1.29473
#> BusinessTravelTravel_Rarely YearsSinceLastPromotion
#> 0.64727 -0.03092
#> OverTimeYes
#> 1.31904
#>
#> Degrees of Freedom: 1469 Total (i.e. Null); 1465 Residual
#> Null Deviance: 1299
#> Residual Deviance: 1194 AIC: 1204