Exercise

Try another threshold

In the previous exercises, you used a threshold of 0.50 to cut your predicted probabilities to make class predictions (rock vs mine). However, this classification threshold does not always align with the goals for a given modeling problem.

For example, pretend you want to identify the objects you are really certain are mines. In this case, you might want to use a probability threshold of 0.90 to get fewer predicted mines, but with greater confidence in each prediction.

The code pattern for cutting probabilities into predicted classes, then calculating a confusion matrix, was shown in Exercise 7 of this chapter.

Instructions

100 XP
  • Use ifelse() to create a character vector, m_or_r that is the positive class, "M", when p is greater than 0.9, and the negative class, "R", otherwise.
  • Convert m_or_r to be a factor, p_class, with levels the same as those of test[["Class"]].
  • Make a confusion matrix with confusionMatrix(), passing p_class and the "Class" column from the test dataset.