我使用逻辑回归构建了一个预测模型，效果很好。但是当我分析测试数据集上计算的估计值时，我发现用于分层拆分的变量出现在了模型中，而我希望它作为预测变量被排除在外。update_role() 并没有解决这个问题…

data_split <- initial_split(mldata, prop = 3/4, strata = strata_var)# 创建训练和测试数据集:train_data <- training(data_split)test_data  <- testing(data_split)# 构建模型mldata_recipe <-  recipe(vital ~ ., data = train_data)  %>%   update_role(ids, new_role = "ID") %>%  update_role(strata_var, new_role = "strata") %>%   step_zv(all_predictors()) %>%  step_unknown(all_nominal_predictors()) %>%   step_dummy(all_nominal(), -all_outcomes()) %>%  step_smote(vital)set.seed(456)# 10折交叉验证mldata_folds <- vfold_cv(train_data, strata = strata_var)glmnet_spec <-   logistic_reg(penalty = tune(), mixture = tune()) %>%   set_mode("classification") %>%   set_engine("glmnet") glmnet_workflow <-   workflow() %>%   add_recipe(mldata_recipe) %>%   add_model(glmnet_spec) glmnet_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), mixture = c(0, 0.05,                                                                                       0.2, 0.4, 0.6, 0.8, 1)) set.seed(789)glmnet_tune <-   tune_grid(glmnet_workflow, resamples = mldata_folds, grid = glmnet_grid) final_glmnet <- glmnet_workflow %>%   finalize_workflow(select_best(glmnet_tune, "roc_auc")) glmnet_results <- final_glmnet %>%   fit_resamples(    resamples = mldata_folds,    metrics = metric_set(roc_auc, accuracy, sensitivity, specificity),    control = control_resamples(save_pred = TRUE)  )set.seed(789)final_fit <- final_glmnet %>%   last_fit(data_split)final_fit %>%   pull(.workflow) %>%   pluck(1) %>%   tidy() %>%   filter(term != "(Intercept)") %>%   arrange(desc(abs(estimate))) %>%   filter(abs(estimate) >0) %>%   ggplot(aes(estimate, fct_reorder(term, desc(estimate)), color = estimate > 0))+  geom_vline(xintercept = 0, color = "lightgrey", lty = 2, size = 1.2) +  geom_point() +   scale_color_discrete(name = "Variable Effect \non outcome", labels = c("Deleterious", "Beneficial")) +  theme_minimal()+  ggtitle("Meaningful Parameter Estimate Coefficients using logistic regression model")

在最后的图表中，我可以看到分层变量出现了。

回答：

你得到这个结果是因为你在step_dummy()中使用的角色选择函数的组合。（完整的reprex在文章末尾）

你使用了以下选择。这会选择所有名义变量，但不包括任何结果。这是因为分层变量既是名义变量又不是结果变量，所以被选中了。

all_nominal(), -all_outcomes()

更好的选择是使用all_nominal_predictors()，它不会选择id/分层变量。

library(tidymodels)data("penguins")rec_spec1 <- recipe(species ~ island + body_mass_g, data = penguins) %>%   update_role(island, new_role = "strata") %>%   step_dummy(all_nominal(), -all_outcomes())rec_spec1 %>%  prep() %>%  bake(new_data = NULL)#> # A tibble: 344 × 4#>    body_mass_g species island_Dream island_Torgersen#>          <int> <fct>          <dbl>            <dbl>#>  1        3750 Adelie             0                1#>  2        3800 Adelie             0                1#>  3        3250 Adelie             0                1#>  4          NA Adelie             0                1#>  5        3450 Adelie             0                1#>  6        3650 Adelie             0                1#>  7        3625 Adelie             0                1#>  8        4675 Adelie             0                1#>  9        3475 Adelie             0                1#> 10        4250 Adelie             0                1#> # … with 334 more rowsrec_spec2 <- recipe(species ~ island + body_mass_g, data = penguins) %>%   update_role(island, new_role = "strata") %>%   step_dummy(all_nominal_predictors())rec_spec2 %>%  prep() %>%  bake(new_data = NULL)#> # A tibble: 344 × 3#>    island    body_mass_g species#>    <fct>           <int> <fct>  #>  1 Torgersen        3750 Adelie #>  2 Torgersen        3800 Adelie #>  3 Torgersen        3250 Adelie #>  4 Torgersen          NA Adelie #>  5 Torgersen        3450 Adelie #>  6 Torgersen        3650 Adelie #>  7 Torgersen        3625 Adelie #>  8 Torgersen        4675 Adelie #>  9 Torgersen        3475 Adelie #> 10 Torgersen        4250 Adelie #> # … with 334 more rows

完整的reprex

library(tidymodels)library(themis)library(forcats)data("penguins")penguins0 <- penguins %>%  mutate(ids = row_number(),         species = factor(species == "Adelie")) %>%  drop_na()data_split <- initial_split(penguins0, prop = 3/4, strata = island)# 创建训练和测试数据集:train_data <- training(data_split)test_data  <- testing(data_split)# 构建模型mldata_recipe <-  recipe(species ~ ., data = train_data)  %>%   update_role(ids, new_role = "ID") %>%  update_role(island, new_role = "strata") %>%   step_zv(all_predictors()) %>%  step_unknown(all_nominal_predictors()) %>%   step_dummy(all_nominal_predictors()) %>%  step_smote(species)set.seed(456)# 10折交叉验证mldata_folds <- vfold_cv(train_data, strata = island)glmnet_spec <-   logistic_reg(penalty = tune(), mixture = tune()) %>%   set_mode("classification") %>%   set_engine("glmnet") glmnet_workflow <-   workflow() %>%   add_recipe(mldata_recipe) %>%   add_model(glmnet_spec) glmnet_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20),                                mixture = c(0, 0.05, 0.2, 0.4, 0.6, 0.8, 1)) set.seed(789)glmnet_tune <-   tune_grid(glmnet_workflow, resamples = mldata_folds, grid = glmnet_grid) final_glmnet <- glmnet_workflow %>%   finalize_workflow(select_best(glmnet_tune, "roc_auc")) glmnet_results <- final_glmnet %>%   fit_resamples(    resamples = mldata_folds,    metrics = metric_set(roc_auc, accuracy, sensitivity, specificity),    control = control_resamples(save_pred = TRUE)  )set.seed(789)final_fit <- final_glmnet %>%   last_fit(data_split)final_fit %>%   pull(.workflow) %>%   pluck(1) %>%   tidy() %>%   filter(term != "(Intercept)") %>%   arrange(desc(abs(estimate))) %>%   filter(abs(estimate) >0) %>%   ggplot(aes(estimate, fct_reorder(term, desc(estimate)), color = estimate > 0))+  geom_vline(xintercept = 0, color = "lightgrey", lty = 2, size = 1.2) +  geom_point() +   scale_color_discrete(name = "Variable Effect \non outcome", labels = c("Deleterious", "Beneficial")) +  theme_minimal()+  ggtitle("Meaningful Parameter Estimate Coefficients using logistic regression model")