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  3. Credit Risk Modeling in R
  • 1

    Introduction and data preprocessing

    Gratuito

    This chapter begins with a general introduction to credit risk models. We'll explore a real-life data set, then preprocess the data set such that it's in the appropriate format before applying the credit risk models.

  • 2

    Logistic regression

    Logistic regression is still a widely used method in credit risk modeling. In this chapter, you will learn how to apply logistic regression models on credit data in R.

  • 3

    Decision trees

    Classification trees are another popular method in the world of credit risk modeling. In this chapter, you will learn how to build classification trees using credit data in R.

  • 4

    Evaluating a credit risk model

    In this chapter, you'll learn how you can evaluate and compare the results obtained through several credit risk models.

Initializing

Exercicio

Making more discriminative models

In the previous exercise, the range for predicted probabilities of default was rather small. As discussed, small predicted default probabilities are to be expected with low default rates, but building bigger models (which basically means: including more predictors) can expand the range of your predictions.

Whether this will eventually lead to better predictions still needs to be validated and depends on the quality of the newly included predictors. But first, have a look at how bigger models can expand the range.

Instruções

100 XP
  • Make log_model_full like the way you made log_model_small, but this time, include all available predictors in the data set. If you don't want to type the name of every column separately, you can simply select all variables using loan_status ~ .
  • Create your prediction vector predictions_all_full for all the cases in the test set using predict(). Notice that these values represent the probability of defaulting.
  • Look at the range of the predictions.