Including a loss matrix

Thirdly, you can include a loss matrix, changing the relative importance of misclassifying a default as non-default versus a non-default as a default. You want to stress that misclassifying a default as a non-default should be penalized more heavily. Including a loss matrix can again be done in the argument parms in the loss matrix.

parms = list(loss = matrix(c(0, cost_def_as_nondef, cost_nondef_as_def, 0), ncol=2))

Doing this, you are constructing a 2x2-matrix with zeroes on the diagonal and changed loss penalties off-diagonal. The default loss matrix is all ones off-diagonal.

This exercise is part of the course

Credit Risk Modeling in R

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Exercise instructions

Change the code provided such a loss matrix is included, with a penalization that is 10 times bigger when misclassifying an actual default as a non-default. This can be done replacing cost_def_as_nondef by 10, and cost_nondef_as_def by 1. Similar to what you've done in the previous exercises, include rpart.control to relax the complexity parameter to 0.001.
Plot the decision tree using the function plot and the tree object name. Add a second argument uniform = TRUE to get equal-sized branches, and add labels to the tree using text() with the tree object name.

Hands-on interactive exercise

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

# Change the code below such that a decision tree is constructed using a loss matrix penalizing 10 times more heavily for misclassified defaults.
tree_loss_matrix <- rpart(loan_status ~ ., method = "class",
                          data =  training_set)


# Plot the decision tree


# Add labels to the decision tree

Edit and Run Code