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  3. Credit Risk Modeling in R
  • 1

    Introduction and data preprocessing

    Free

    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

Exercise

Best of four

The AUC-values for the four models built in the previous exercise are stored in

AUC_4_remove_amnt 
AUC_4_remove_grade
AUC_4_remove_inc 
AUC_4_remove_emp

Which model should be preferred based on AUC?

Instructions

50 XP

Possible answers