在R中如何做logistic回归自举后的混淆矩阵？

library(boot)
data <- read.csv("your_data.csv")  # 替换为你的数据集文件路径

logistic_model <- function(data, indices) {
  train_data <- data[indices, ]
  model <- glm(formula = target_variable ~ ., data = train_data, family = binomial)
  return(model)
}

boot_results <- boot(data, statistic = logistic_model, R = 100)  # R为自举重复次数，可以根据需要调整

boot_coefs <- t(sapply(boot_results$t, coef))

confusion_matrix <- table(data$target_variable, predict(boot_coefs, newdata = data, type = "response") > 0.5)

print(confusion_matrix)