Arguments
- x
A workflow set outputted by
workflow_set()
orworkflow_map()
.- id
A single character string for a workflow ID.
Value
pull_workflow_set_result()
produces a tune_result
or
resample_results
object. pull_workflow()
returns an unfit workflow
object.
Details
pull_workflow_set_result()
retrieves the results of workflow_map()
for a
particular workflow while pull_workflow()
extracts the unfitted workflow
from the info
column.
The extract_workflow_set_result()
and extract_workflow()
functions should
be used instead of these functions.
Examples
library(tune)
two_class_res
#> # A workflow set/tibble: 6 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 none_cart <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 2 none_glm <tibble [1 × 4]> <opts[3]> <rsmp[+]>
#> 3 none_mars <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 4 yj_trans_cart <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 5 yj_trans_glm <tibble [1 × 4]> <opts[3]> <rsmp[+]>
#> 6 yj_trans_mars <tibble [1 × 4]> <opts[3]> <tune[+]>
pull_workflow_set_result(two_class_res, "none_cart")
#> Warning: `pull_workflow_set_result()` was deprecated in workflowsets 0.1.0.
#> ℹ Please use `extract_workflow_set_result()` instead.
#> # Tuning results
#> # 5-fold cross-validation
#> # A tibble: 5 × 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [632/159]> Fold1 <tibble [20 × 6]> <tibble [0 × 1]>
#> 2 <split [633/158]> Fold2 <tibble [20 × 6]> <tibble [0 × 1]>
#> 3 <split [633/158]> Fold3 <tibble [20 × 6]> <tibble [0 × 1]>
#> 4 <split [633/158]> Fold4 <tibble [20 × 6]> <tibble [0 × 1]>
#> 5 <split [633/158]> Fold5 <tibble [20 × 6]> <tibble [0 × 1]>
pull_workflow(two_class_res, "none_cart")
#> Warning: `pull_workflow()` was deprecated in workflowsets 0.1.0.
#> ℹ Please use `extract_workflow()` instead.
#> ══ Workflow ══════════════════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: decision_tree()
#>
#> ── Preprocessor ──────────────────────────────────────────────────────────
#> Class ~ A + B
#>
#> ── Model ─────────────────────────────────────────────────────────────────
#> Decision Tree Model Specification (classification)
#>
#> Main Arguments:
#> cost_complexity = tune()
#> min_n = tune()
#>
#> Computational engine: rpart
#>