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.

pull_workflow_set_result(x, id)

pull_workflow(x, id)

Arguments

x

A workflow set.

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.

Examples

library(tune) pull_workflow_set_result(two_class_res, "none_cart")
#> # Tuning results #> # 5-fold cross-validation #> # A tibble: 5 x 4 #> splits id .metrics .notes #> <list> <chr> <list> <list> #> 1 <split [632/159]> Fold1 <tibble[,6] [20 × 6]> <tibble[,1] [0 × 1]> #> 2 <split [633/158]> Fold2 <tibble[,6] [20 × 6]> <tibble[,1] [0 × 1]> #> 3 <split [633/158]> Fold3 <tibble[,6] [20 × 6]> <tibble[,1] [0 × 1]> #> 4 <split [633/158]> Fold4 <tibble[,6] [20 × 6]> <tibble[,1] [0 × 1]> #> 5 <split [633/158]> Fold5 <tibble[,6] [20 × 6]> <tibble[,1] [0 × 1]>
pull_workflow(two_class_res, "none_cart")
#> ══ 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 #>