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我recipe()在包中使用函数tidymodels来估算缺失值和修复不平衡数据。

这是我的数据；

mer_df <- mer2 %>%
  filter(!is.na(laststagestatus2)) %>% 
  select(Id, Age_Range__c, Gender__c, numberoflead, leadduration, firsttouch, lasttouch, laststagestatus2)%>%
  mutate_if(is.character, factor) %>%
  mutate_if(is.logical, as.integer)


# A tibble: 197,836 x 8
   Id    Age_Range__c Gender__c numberoflead leadduration firsttouch lasttouch
   <fct> <fct>        <fct>            <int>        <dbl> <fct>      <fct>    
 1 0010~ NA           NA                   2     5.99     Dealer IB~ Walk in  
 2 0010~ NA           NA                   1     0        Online Se~ Online S~
 3 0010~ NA           NA                   1     0        Walk in    Walk in  
 4 0010~ NA           NA                   1     0        Online Se~ Online S~
 5 0010~ NA           NA                   2     0.0128   Dealer IB~ Dealer I~
 6 0010~ NA           NA                   1     0        OB Call    OB Call  
 7 0010~ NA           NA                   1     0        Dealer IB~ Dealer I~
 8 0010~ NA           NA                   4    73.9      Dealer IB~ Walk in  
 9 0010~ NA           Male                24     0.000208 OB Call    OB Call  
10 0010~ NA           NA                  18     0.000150 OB Call    OB Call  
# ... with 197,826 more rows, and 1 more variable: laststagestatus2 <fct>

这是我的代码；

mer_rec <- recipe(laststagestatus2 ~ ., data = mer_train)%>%
  step_medianimpute(numberoflead,leadduration)%>%
  step_knnimpute(Gender__c,Age_Range__c,fisrsttouch,lasttouch) %>% 
  step_other(Id,firsttouch) %>% 
  step_other(Id,lasttouch) %>% 
  step_dummy(all_nominal(), -laststagestatus2) %>% 
  step_smote(laststagestatus2)
mer_rec %>% prep() %>% juice()


glm_spec <- logistic_reg() %>%
  set_engine("glm")

rf_spec <- rand_forest(trees = 1000) %>%
  set_mode("classification") %>%
  set_engine("ranger")

mer_wf <- workflow() %>%
  add_recipe(mer_rec)



 mer_metrics <- metric_set(roc_auc, accuracy, sensitivity, specificity)

直到这里它都可以正常工作现在我正在使用fit_resamples函数来拟合每个重采样的逻辑回归。

这是我的代码如下：

doParallel::registerDoParallel()
    glm_rs <- mer_wf %>%
      add_model(glm_spec) %>%
      fit_resamples(
        resamples = mer_folds,
        metrics = mer_metrics,
        control = control_resamples(save_pred = TRUE)
glm_rs

我收到警告说：

Warning message:
All models failed in [fit_resamples()]. See the `.notes` column. 


    Resampling results
10-fold cross-validation using stratification 
 A tibble: 10 x 5
   splits                 id     .metrics .notes           .predictions
   <list>                 <chr>  <list>   <list>           <list>      
 1 <split [133.5K/14.8K]> Fold01 <NULL>   <tibble [1 x 1]> <NULL>      
 2 <split [133.5K/14.8K]> Fold02 <NULL>   <tibble [1 x 1]> <NULL>      
 3 <split [133.5K/14.8K]> Fold03 <NULL>   <tibble [1 x 1]> <NULL>      
 4 <split [133.5K/14.8K]> Fold04 <NULL>   <tibble [1 x 1]> <NULL>      
 5 <split [133.5K/14.8K]> Fold05 <NULL>   <tibble [1 x 1]> <NULL>      
 6 <split [133.5K/14.8K]> Fold06 <NULL>   <tibble [1 x 1]> <NULL>      
 7 <split [133.5K/14.8K]> Fold07 <NULL>   <tibble [1 x 1]> <NULL>      
 8 <split [133.5K/14.8K]> Fold08 <NULL>   <tibble [1 x 1]> <NULL>      
 9 <split [133.5K/14.8K]> Fold09 <NULL>   <tibble [1 x 1]> <NULL>      
10 <split [133.5K/14.8K]> Fold10 <NULL>   <tibble [1 x 1]> <NULL> 

Warning message:
This tuning result has notes. Example notes on model fitting include:
recipe: Error: could not find function "all_nominal"

有人对如何做到这一点有任何建议吗？非常感谢您的帮助！

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