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  3. Credit Risk Modeling in R

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).