d nd? workflow(penguin_formula, rf_spec) %>% fit(data = penguins_train) #> ══ Workflow [trained] ══════════════════════════════════════════════════════════════════ #> Preprocessor: Formula #> Model: rand_forest() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────────────── #> species ~ bill_length_mm + bill_depth_mm + sex #> #> ── Model ─────────────────────────────────────────────────────────────────────────────── #> Ranger result #> #> Call: #> ranger::ranger(x = maybe_data_frame(x), y = y, num.threads = 1, #> verbose = FALSE, seed = sample.int(10^5, 1), probability = TRUE) #> #> Type: Probability estimation #> Number of trees: 500 #> Sample size: 249 #> Number of independent variables: 3 #> Mtry: 1 #> Target node size: 10 #> Variable importance mode: none #> Splitrule: gini #> OOB prediction error (Brier s.): 0.05585744 @j l