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Exercise

From probabilites to confusion matrix

Conversely, say you want to be really certain that your model correctly identifies all the mines as mines. In this case, you might use a prediction threshold of 0.10, instead of 0.90.

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 0.1, 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.