Compute classification boundaries for a workflow_set — boundary_workflow

A dedicated helper to automatically fit and extract classification boundaries for an entire workflow_set. This avoids the need for manual iteration over models.

Usage

boundary_workflow_set(
  wf_set,
  data,
  feature_range = NULL,
  response,
  resolution = 100,
  ...
)

Arguments

wf_set: A workflow_set object from the workflowsets package.
data: A data frame containing the training data. This is required to extract feature metadata and to fit any workflows that are not yet trained.
feature_range: An optional named list specifying the minimum and maximum values for each feature, or a character vector of feature names. If NULL, the ranges are automatically computed from the training data (if there are exactly 2 numeric features).
response: A string specifying the name of the response column in data.
resolution: An integer specifying the number of points along each axis (default = 100).
...: Additional arguments passed to boundary_compute().

Value

A data frame containing the combined boundary grid for all models, with a model column indicating the wflow_id.

Examples

# \donttest{
library(palmerpenguins)
library(workflowsets)
library(parsnip)

data(penguins)
peng_data <- na.omit(penguins[, c("species", "bill_length_mm", "bill_depth_mm")])

# Define multiple engines
spec_rpart <- decision_tree() |>
  set_engine("rpart") |>
  set_mode("classification")
spec_glm <- multinom_reg() |>
  set_engine("nnet") |>
  set_mode("classification")

# Create a workflow set
wf_set <- workflow_set(
  preproc = list(base = species ~ bill_length_mm + bill_depth_mm),
  models = list(tree = spec_rpart, log_reg = spec_glm)
)

# Compute 2D boundaries for all models simultaneously (auto-range)
bounds <- boundary_workflow_set(wf_set, peng_data, response = "species", resolution = 30)
# }