Two Class Example Data

The package supplies two pre-generated workflow sets, two_class_set and chi_features_set, and associated sets of model fits two_class_res and chi_features_res.

The two_class_* objects are based on a binary classification problem using the two_class_dat data from the modeldata package. The six models utilize either a bare formula or a basic recipe utilizing recipes::step_YeoJohnson() as a preprocessor, and a decision tree, logistic regression, or MARS model specification. See ?two_class_set for source code.

The chi_features_* objects are based on a regression problem using the Chicago data from the modeldata package. Each of the three models utilize a linear regression model specification, with three different recipes of varying complexity. The objects are meant to approximate the sequence of models built in Section 1.3 of Kuhn and Johnson (2019). See ?chi_features_set for source code.

Details

See below for the source code to generate the Two Class example workflow sets:

library(workflowsets)
library(workflows)
library(modeldata)
library(recipes)
library(parsnip)
library(dplyr)
library(rsample)
library(tune)
library(yardstick)

# ------------------------------------------------------------------------------

data(two_class_dat, package = "modeldata")

set.seed(1)
folds <- vfold_cv(two_class_dat, v = 5)

# ------------------------------------------------------------------------------

decision_tree_rpart_spec <-
  decision_tree(min_n = tune(), cost_complexity = tune()) %>%
  set_engine('rpart') %>%
  set_mode('classification')

logistic_reg_glm_spec <-
  logistic_reg() %>%
  set_engine('glm')

mars_earth_spec <-
  mars(prod_degree = tune()) %>%
  set_engine('earth') %>%
  set_mode('classification')

# ------------------------------------------------------------------------------

yj_recipe <-
   recipe(Class ~ ., data = two_class_dat) %>%
   step_YeoJohnson(A, B)

# ------------------------------------------------------------------------------

two_class_set <-
   workflow_set(
      preproc = list(none = Class ~ A + B, yj_trans = yj_recipe),
      models = list(cart = decision_tree_rpart_spec, glm = logistic_reg_glm_spec,
                    mars = mars_earth_spec)
   )

# ------------------------------------------------------------------------------

two_class_res <-
  two_class_set %>%
  workflow_map(
    resamples = folds,
    grid = 10,
    seed = 2,
    verbose = TRUE,
    control = control_grid(save_workflow = TRUE)
  )

Examples

data(two_class_set)

two_class_set
#> # A workflow set/tibble: 6 × 4
#>   wflow_id      info             option    result    
#>   <chr>         <list>           <list>    <list>    
#> 1 none_cart     <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 none_glm      <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 none_mars     <tibble [1 × 4]> <opts[0]> <list [0]>
#> 4 yj_trans_cart <tibble [1 × 4]> <opts[0]> <list [0]>
#> 5 yj_trans_glm  <tibble [1 × 4]> <opts[0]> <list [0]>
#> 6 yj_trans_mars <tibble [1 × 4]> <opts[0]> <list [0]>