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

Exploring the credit data

Interpreting a CrossTable()

Histograms and outliers

Histograms

Outliers

Missing data and coarse classification

Deleting missing data

Replacing missing data

Keeping missing data

Data splitting and confusion matrices

Splitting the data set

Creating a confusion matrix

Introduction and data preprocessing

Chapter 1

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

Basic logistic regression

Interpreting the odds for a categorical variable

Multiple variables in a logistic regression model

Interpreting significance levels

Logistic regression: predicting the probability of default

Predicting the probability of default

Making more discriminative models

Evaluating the logistic regression model result

Specifying a cut-off

Comparing two cut-offs

Wrap-up and remarks

Comparing link functions for a given cut-off

Logistic regression

Chapter 2

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?

Computing the gain for a tree

Changing one Gini...

Building decision trees using the rpart()-package

Undersampling the training set

Changing the prior probabilities

Including a loss matrix

Pruning the decision tree

Pruning the tree with changed prior probabilities

Pruning the tree with the loss matrix

Other tree options and the construction of confusion matrices

One final tree using more options

Confusion matrices and accuracy of our final trees

Optimizing the accuracy

Decision trees

Chapter 3

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

Menentukan cut-off yang tepat: kurva strategi

Menghitung bad rate dengan tingkat akseptansi tetap

Tabel strategi dan kurva strategi

To tree or not to tree?

Kurva ROC

Kurva ROC untuk membandingkan model regresi logistik

Kurva ROC untuk membandingkan model berbasis pohon

Pemilihan input berdasarkan AUC

Putaran pemangkasan lain berdasarkan AUC

Terbaik dari empat

Reduksi model lebih lanjut?

Penutup kursus

Evaluating a credit risk model

Chapter 4 Loan Data Chapter 1

Loan Data Chapter 2, 3 and 4

Kursus praktik dengan data kredit nyata ini akan mengajarkan Anda cara memodelkan risiko kredit menggunakan regresi logistik dan pohon keputusan di R. Pemodelan risiko kredit untuk pinjaman perorangan maupun perusahaan sangat penting bagi bank. Probabilitas bahwa seorang debitur akan gagal bayar adalah komponen kunci untuk mengukur risiko kredit. Meskipun model lain juga akan diperkenalkan dalam kursus ini, Anda akan mempelajari dua jenis model yang sering digunakan dalam konteks credit scoring: regresi logistik dan pohon keputusan. Anda akan belajar cara menggunakannya dalam konteks ini, serta bagaimana bank mengevaluasi model-model tersebut.

Intermediate R for Finance

Belajar membuat model untuk menilai risiko kredit keuangan secara profesional.

Pemodelan Risiko Kredit di R

Terapkan pemodelan statistik dalam konteks nyata menggunakan regresi logistik dan pohon keputusan untuk memodelkan risiko kredit.

Menentukan cut-off yang tepat: kurva strategi

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