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Exercise

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.

Instructions

100 XP
  • 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.