Introduction to tidymodels
wf_set_fit <- wf_set %>%
workflow_map("fit_resamples", resamples = forested_folds)
wf_set_fit
#> # A workflow set/tibble: 2 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 formula_decision_tree <tibble [1 × 4]> <opts[1]> <rsmp[+]>
#> 2 formula_rand_forest <tibble [1 × 4]> <opts[1]> <rsmp[+]>
wf_set_fit %>%
rank_results()
#> # A tibble: 6 × 9
#> wflow_id .config .metric mean std_err n preprocessor model rank
#> <chr> <chr> <chr> <dbl> <dbl> <int> <chr> <chr> <int>
#> 1 formula_rand_fo… Prepro… accura… 0.919 0.00525 10 formula rand… 1
#> 2 formula_rand_fo… Prepro… brier_… 0.0617 0.00338 10 formula rand… 1
#> 3 formula_rand_fo… Prepro… roc_auc 0.972 0.00310 10 formula rand… 1
#> 4 formula_decisio… Prepro… accura… 0.894 0.00562 10 formula deci… 2
#> 5 formula_decisio… Prepro… brier_… 0.0817 0.00434 10 formula deci… 2
#> 6 formula_decisio… Prepro… roc_auc 0.951 0.00378 10 formula deci… 2
The first metric of the metric set is used for ranking. Use rank_metric
to change that.
Lots more available with workflow sets, like collect_metrics()
, autoplot()
methods, and more!
When do you think a workflow set would be useful?
Discuss with your neighbors!
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