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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 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.

Usage

workflow(preprocessor = NULL, spec = NULL)

Arguments

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 add_model().

Value

A new workflow object.

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            8730.0

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)

## == 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. 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