如何使用 R 中的 tidymodels 调整后的 model 预测测试集的置信区间？

Question

我在 R 中使用tidymodels拟合随机森林 model，当我尝试使用调整后的 model 预测测试集时出现错误： splits的每个元素必须是一个rsplit object。

# Data splitting
data(Sacramento, package = "modeldata")
set.seed(123)
data_split <- initial_split(Sacramento, prop = 0.75, strata = price)
Sac_train <- training(data_split)
Sac_test <- testing(data_split)

# Build the model
rf_mod <- rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% 
          set_engine("ranger", importance = "permutation") %>% 
          set_mode("regression")

# Create the recipe
Sac_recipe <- recipe(price ~ ., data = Sac_train) %>% 
              step_rm(zip, latitude, longitude) %>% 
              step_corr(all_numeric_predictors(), threshold = 0.85) %>% 
              step_zv(all_numeric_predictors()) %>% 
              step_normalize(all_numeric_predictors()) %>%
              step_dummy(all_nominal_predictors())

# Create the workflow
rf_workflow <- workflow() %>% 
               add_model(rf_mod) %>% 
               add_recipe(Sac_recipe)

# Train and Tune the model
set.seed(123)
Sac_folds <- vfold_cv(Sac_train, v = 10, repeats = 2, strata = price)

rf_res <- rf_workflow %>% 
          tune_grid(grid = 2*2,
                    resamples = Sac_folds, 
                    control = control_grid(save_pred = TRUE),
                    metrics = metric_set(rmse))

# Extract the best model
rf_best <- rf_res %>%
           select_best(metric = "rmse")

# Last fit
last_rf_workflow <- rf_workflow %>% 
                    finalize_workflow(rf_best)

last_rf_fit <- last_rf_workflow %>% 
               last_fit(Sac_train)
# Error: Each element of `splits` must be an `rsplit` object.

predict(last_rf_fit, Sac_test, type = "conf_int")

Answer 1

错误从这些行产生，

last_rf_fit <- last_rf_workflow %>% 
               last_fit(Sac_train)

现在从last_fit的文档中，

# S3 method for workflow
last_fit(object, split, ..., metrics = NULL, control = control_last_fit())

因此， workflow last_fit作为第一个参数通过%>%传递给 last_fit， Sac_train传递给split参数。

但是从文档来看， split参数需要是，

从rsample::initial_split()创建的 rsplit object

相反，试试这个，

last_rf_fit <- last_rf_workflow %>% 
  last_fit(data_split)

然后 按照文档收集预测，

collect_predictions(last_rf_fit)