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我正在努力解决如何使用 tidymodels 从逻辑回归模型中获取 AUC。

这是使用内置mpg数据集的示例。

library(tidymodels)
library(tidyverse)

# Use mpg dataset
df <- mpg

# Create an indicator variable for class="suv"
df$is_suv <- as.factor(df$class == "suv")

# Create the split object
df_split <- initial_split(df, prop=1/2)

# Create the training and testing sets
df_train <- training(df_split)
df_test <- testing(df_split)

# Create workflow
rec <-
  recipe(is_suv ~ cty + hwy + cyl, data=df_train)

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

glm_wflow <- 
  workflow() %>%
  add_recipe(rec) %>%
  add_model(glm_spec)

# Fit the model
model1 <- fit(glm_wflow, df_train)

# Attach predictions to training dataset
training_results <- bind_cols(df_train, predict(model1, df_train))

# Calculate accuracy
accuracy(training_results, truth = is_suv, estimate = .pred_class)

# Calculate AUC??
roc_auc(training_results, truth = is_suv, estimate = .pred_class)

最后一行返回此错误：

> roc_auc(training_results, truth = is_suv, estimate = .pred_class)
Error in metric_summarizer(metric_nm = "roc_auc", metric_fn = roc_auc_vec,  : 
  formal argument "estimate" matched by multiple actual arguments
4

1 回答 1

2

由于您正在进行二进制分类，roc_auc()因此期望与“相关”类而不是预测类相对应的类概率向量。

您可以使用predict(model1, df_train, type = "prob"). 或者，如果您使用的是 0.2.2 或更高版本的工作流，您可以使用augment()获取类别预测和概率，而无需使用bind_cols().

library(tidymodels)
library(tidyverse)

# Use mpg dataset
df <- mpg

# Create an indicator variable for class="suv"
df$is_suv <- as.factor(df$class == "suv")

# Create the split object
df_split <- initial_split(df, prop=1/2)

# Create the training and testing sets
df_train <- training(df_split)
df_test <- testing(df_split)

# Create workflow
rec <-
  recipe(is_suv ~ cty + hwy + cyl, data=df_train)

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

glm_wflow <- 
  workflow() %>%
  add_recipe(rec) %>%
  add_model(glm_spec)

# Fit the model
model1 <- fit(glm_wflow, df_train)

# Attach predictions to training dataset
training_results <- augment(model1, df_train)

# Calculate accuracy
accuracy(training_results, truth = is_suv, estimate = .pred_class)
#> # A tibble: 1 x 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 accuracy binary         0.795

# Calculate AUC
roc_auc(training_results, truth = is_suv, estimate = .pred_FALSE)
#> # A tibble: 1 x 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 roc_auc binary         0.879

由reprex 包于 2021-04-12 创建(v1.0.0)

于 2021-04-12T18:28:12.547 回答