在R中绘制多类ROC曲线时出错

我制作了一个SVM预测器，它可以将样本分为三组——“好”、“坏”或“好”。但是，测试数据集只包含分类为“好”或“坏”的样本。当我试图使用

multi_-roc

时，我遇到了一个错误，我不确定解决这个问题的最佳方法。我举的例子如下：

library(tidymodels)
library(mlbench)
library(multiROC)
data(Ionosphere)

# preprocess dataset
Ionosphere <- Ionosphere %>% select(-V1, -V2)

# split into training and test data
ion_split <- initial_split(Ionosphere, prop = 3/5)

ion_train <- training(ion_split)
ion_test <- testing(ion_split) 

# making an artificial third class in the training set for this example
ion_train[,33] <- as.character(ion_train[,33])
ion_train[1:7,33] <- "ok"
ion_train[,33] <- as.factor(ion_train[,33])

# make a recipe
iono_rec <-
  recipe(Class ~ ., data = ion_train)  %>%
  step_normalize(all_predictors()) 

# build the model and workflow
svm_mod <-
  svm_rbf(cost = tune(), rbf_sigma = tune()) %>%
  set_mode("classification") %>%
  set_engine("kernlab")

svm_workflow <- 
      workflow() %>%
      add_recipe(iono_rec) %>%
      add_model(svm_mod)

# run model tuning
set.seed(35)
recipe_res <-
  svm_workflow %>% 
  tune_grid(
    resamples = bootstraps(ion_train, times = 2),
    metrics = metric_set(roc_auc),
    control = control_grid(verbose = TRUE, save_pred = TRUE)
  )

# chose best model, finalise workflow
best_mod <- recipe_res %>% select_best("roc_auc")
final_wf <- finalize_workflow(svm_workflow, best_mod)
final_mod <- final_wf %>% fit(ion_train)

predict_res <- predict(
        final_mod,
        ion_test,
        type = "prob")


results <- predict_res %>% 
    cbind(ion_test$Class) %>%
    dplyr::rename(
        bad_pred_svm = .pred_bad,
        good_pred_svm = .pred_good,
        ok_pred_svm = .pred_ok,
        class = `ion_test$Class`
    ) %>%
    mutate(
        bad_true = ifelse(class == "bad", 1, 0),
        good_true = ifelse(class == "good", 1, 0),
        ok_true = ifelse(class == "ok", 1, 0)
    ) %>%
dplyr::select(-class)

当我尝试将其放入multi_roc时，我得到一个错误：

multi_roc_svm <- multi_roc(results, force_diag = TRUE)

Error in approx(res_sp[[i]][[j]], res_se[[i]][[j]], all_sp, yleft = 1,  : 
  need at least two non-NA values to interpolate
In addition: Warning messages:
1: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
2: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' value

---

multi\u-roc\u-svm我不知道multi\u-roc（）的包是什么，但是tidymodels解决方案非常简单

如果您只想从多类ROC曲线中获取ROC值，可以使用
尺度
函数：

> predict_res %>% 
+     bind_cols(ion_test) %>% 
+     # or roc_curve(Class, .pred_bad)
+     roc_auc(Class, .pred_bad)
# A tibble: 1 x 3
  .metric .estimator .estimate
  <chr>   <chr>          <dbl>
1 roc_auc binary         0.976

>预测\u res%>%
+结合力（离子测试）%>%
+#或roc#U曲线（等级，.pred#u坏）
+roc_auc（等级，.pred_bad）
#一个tibble:1 x 3
.度量.估计.估计
1 roc_auc二进制0.976
如果您能创建一个新的解决方案，它将帮助人们了解问题的范围和原因，并找到答案。话虽如此，你试过在这里使用吗？它适用于多类结果。
> predict_res %>% 
+     bind_cols(ion_test) %>% 
+     # or roc_curve(Class, .pred_bad)
+     roc_auc(Class, .pred_bad)
# A tibble: 1 x 3
  .metric .estimator .estimate
  <chr>   <chr>          <dbl>
1 roc_auc binary         0.976