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.

This exercise is part of the course

Machine Learning with caret in R

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# If p exceeds threshold of 0.9, M else R: m_or_r


# Convert to factor: p_class


# Create confusion matrix

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