Calculate a confusion matrix

As you saw in the video, a confusion matrix is a very useful tool for calibrating the output of a model and examining all possible outcomes of your predictions (true positive, true negative, false positive, false negative).

Before you make your confusion matrix, you need to "cut" your predicted probabilities at a given threshold to turn probabilities into a factor of class predictions. Combine ifelse() with factor() as follows:

pos_or_neg <- ifelse(probability_prediction > threshold, positive_class, negative_class)
p_class <- factor(pos_or_neg, levels = levels(test_values))

confusionMatrix() in caret improves on table() from base R by adding lots of useful ancillary statistics in addition to the base rates in the table. You can calculate the confusion matrix (and the associated statistics) using the predicted outcomes as well as the actual outcomes, e.g.:

confusionMatrix(p_class, test_values)

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 0.5, 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 0.5, M else R: m_or_r


# Convert to factor: p_class


# Create confusion matrix

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