add_model: Add a model to a workflow

In workflows: Modeling Workflows

Add a model to a workflow

Description

• add_model() adds a parsnip model to the workflow.

• remove_model() removes the model specification as well as any fitted model object. Any extra formulas are also removed.

• update_model() first removes the model then adds the new specification to the workflow.

Usage

add_model(x, spec, ..., formula = NULL)

remove_model(x)

update_model(x, spec, ..., formula = NULL)

Arguments

x	A workflow.
spec	A parsnip model specification.
...	These dots are for future extensions and must be empty.
formula	An optional formula override to specify the terms of the model. Typically, the terms are extracted from the formula or recipe preprocessing methods. However, some models (like survival and bayesian models) use the formula not to preprocess, but to specify the structure of the model. In those cases, a formula specifying the model structure must be passed unchanged into the model call itself. This argument is used for those purposes.

Details

add_model() is a required step to construct a minimal workflow.

Value

x, updated with either a new or removed model.

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)

lm_model <- linear_reg()
lm_model <- set_engine(lm_model, "lm")

regularized_model <- set_engine(lm_model, "glmnet")

workflow <- workflow()
workflow <- add_model(workflow, lm_model)
workflow

workflow <- add_formula(workflow, mpg ~ .)
workflow

remove_model(workflow)

fitted <- fit(workflow, data = mtcars)
fitted

remove_model(fitted)

remove_model(workflow)

update_model(workflow, regularized_model)
update_model(fitted, regularized_model)

workflows documentation built on May 29, 2024, 3:57 a.m.