Full Walkthrough For Classification

data <- iris |> 
  as_tibble()

data_split <- initial_split(data, prop = 0.8, strata = Species)
data_train <- training(data_split)
data_test <- testing(data_split)

data_folds <- vfold_cv(data_train, repeats = 5, strata = Species)

data_rec <- recipe(Species ~ ., data = data_train) |> 
  step_normalize(all_numeric()) |> 
  prep()

data_knn <- nearest_neighbor(
  mode = "classification",
  neighbors = tune(),
  weight_func = tune(),
  dist_power = tune()
)

data_wf <- workflow() |> 
  add_recipe(data_rec) |> 
  add_model(data_knn)

tuning_params <- data_knn |> 
  extract_parameter_set_dials()

data_tuning_grid <- grid_latin_hypercube(
  tuning_params,
  size = 100
)

data_tune <- tune_grid(
  data_wf,
  data_folds,
  tuning_params
)

data_tune |> 
  collect_metrics()

autoplot(data_tune)

best_params <- data_tune |> 
  select_best("roc_auc")

data_last_fit <- data_wf |> 
  finalize_workflow(best_params) |> 
  last_fit(data_split)

data_last_fit |> 
  collect_metrics()

iris_test_res <- data_test |> 
  select(Species) |> 
  bind_cols(
    data_last_fit |> 
      extract_workflow() |> 
      predict(data_test)
  ) |> 
  transmute(
    ref = Species,
    pred = .pred_class 
  )

library(caret)

confusionMatrix(data = iris_test_res$pred, reference = iris_test_res$ref)