Exercise

# Specifying a cut-off

We have shown you how the specification of a cut-off can make the difference to obtain a good confusion matrix. Now, you will learn how to transform the prediction vector to a vector of binary values indicating the status of the loan. The `ifelse()`

function in R can help you here.

Applying the `ifelse()`

function in the context of a cut-off, you would have something like

```
ifelse(predictions > 0.3, 1, 0)
```

In the first argument, you are testing whether a certain value in the predictions-vector is bigger than 0.3. If this is `TRUE`

, R returns "1" (specified in the second argument), if `FALSE`

, R returns "0" (specified in the third argument), representing "default" and "no default", respectively.

Instructions

**100 XP**

- The code for the full logistic regression model along with the predictions-vector is given in your console.
- Using a cutoff of 0.15, create vector
`pred_cutoff_15`

using the the`ifelse()`

function and`predictions_all_full`

. - Look at the confusion matrix using
`table()`

(enter the true values, so`test_set$loan_status`

, in the first argument).