为什么我的逻辑回归模型不输出 2 个水平的因子？ （错误：`data` 和 `reference` 应该是具有相同水平的因素。）

Question

From reading similar questions I know that the problem is that the yhat.logisticReg isnt a factor of 2 levels while training.prepped$TARGET_FLAG is. I assume the issue could be fixed by changing my model or in the prediction so that yhat.logisticReg is a factor of 2 levels. How can I do this?

logisticReg = glm(TARGET_FLAG ~ .,
                  data = training.prepped,
                  family = binomial())
yhat.logisticReg = predict(logisticReg, training.prepped, type = "response")
confusionMatrix(yhat.logisticReg, training.prepped$TARGET_FLAG)

Error: `data` and `reference` should be factors with the same levels.

str(training.prepped$TARGET_FLAG)
Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 2 2 1 ...

str(yhat.logisticReg)
 Named num [1:8161] 0.1656 0.2792 0.3717 0.0894 0.272 ...
 - attr(*, "names")= chr [1:8161] "1" "2" "3" "4" ...

Answer 1

You may need to choose a threshold first, and then convert your real-valued data into binary values, eg

a <- c(0.2, 0.7, 0.4)
threshold <- 0.5
binary_a <- factor(as.numeric(a>threshold))

str(binary_a)
Factor w/ 2 levels "0","1": 1 2 1

Answer 2

The library caret have the method confusionMatrix that have several metrics implemented. Calling overall you can get the accuracy. If you want another metric, you can check if they have it implemented and just call it.

library(caret)
acc = c()
for(value in yhat.logisticReg)
{
  predictions <- ifelse(yhat.logisticReg <= value, 0, 1)
  confusion_matrix = confusionMatrix(predictions, yhat.logisticReg)
  acc = c(acc,confusion_matrix$overall["Accuracy"])
}

best_acc = max(acc)
best_threshold  = yhat.logisticReg[which.max(acc)]