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.