课程

Credit Risk Modeling in R

中级技能水平

更新时间 2023年11月

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

免费开始课程

RApplied Finance

4 小时

16 视频

52 练习

4,000 经验值

48,308

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课程描述

This hands-on-course with real-life credit data will teach you how to model credit risk by using logistic regression and decision trees in R. Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. While other models will be introduced in this course as well, you will learn about two model types that are often used in the credit scoring context; logistic regression and decision trees. You will learn how to use them in this particular context, and how these models are evaluated by banks.

先决条件

Intermediate R for Finance

1

Introduction and data preprocessing

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.

Introduction and data structure

50 经验值

Exploring the credit data

100 经验值

Interpreting a CrossTable()

50 经验值

Histograms and outliers

50 经验值

100 经验值

100 经验值

Missing data and coarse classification

50 经验值

Deleting missing data

100 经验值

Replacing missing data

100 经验值

Keeping missing data

100 经验值

Data splitting and confusion matrices

50 经验值

Splitting the data set

100 经验值

Creating a confusion matrix

100 经验值

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.

Logistic regression: introduction

50 经验值

Basic logistic regression

100 经验值

Interpreting the odds for a categorical variable

50 经验值

Multiple variables in a logistic regression model

100 经验值

Interpreting significance levels

50 经验值

Logistic regression: predicting the probability of default

50 经验值

Predicting the probability of default

100 经验值

Making more discriminative models

100 经验值

Evaluating the logistic regression model result

50 经验值

Specifying a cut-off

100 经验值

Comparing two cut-offs

50 经验值

Wrap-up and remarks

50 经验值

Comparing link functions for a given cut-off

100 经验值

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.

What is a decision tree?

50 经验值

Computing the gain for a tree

100 经验值

Changing one Gini...

50 经验值

Building decision trees using the rpart()-package

50 经验值

Undersampling the training set

100 经验值

Changing the prior probabilities

100 经验值

Including a loss matrix

100 经验值

Pruning the decision tree

50 经验值

Pruning the tree with changed prior probabilities

100 经验值

Pruning the tree with the loss matrix

100 经验值

Other tree options and the construction of confusion matrices

50 经验值

One final tree using more options

100 经验值

Confusion matrices and accuracy of our final trees

100 经验值

Optimizing the accuracy

50 经验值

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.

Finding the right cut-off: the strategy curve

50 经验值

Computing a bad rate given a fixed acceptance rate

100 经验值

The strategy table and strategy curve

100 经验值

To tree or not to tree?

50 经验值

The ROC-curve

50 经验值

ROC-curves for comparison of logistic regression models

100 经验值

ROC-curves for comparison of tree-based models

100 经验值

Input selection based on the AUC

50 经验值

Another round of pruning based on AUC

100 经验值

Best of four

50 经验值

Further model reduction?

100 经验值

Course wrap-up

50 经验值

Credit Risk Modeling in R

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