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Kursus

Pemodelan Risiko Kredit di R

MenengahTingkat Keterampilan

Diperbarui 11/2023

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

Mulai Kursus Gratis

RApplied Finance4 jam16 videos52 Latihan4,000 XP48,293Bukti Prestasi

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Deskripsi Kursus

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.

Persyaratan

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

Exploring the credit data

Interpreting a CrossTable()

Histograms and 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

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

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

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?

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

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

Computing a bad rate given a fixed acceptance rate

The strategy table and strategy curve

To tree or not to tree?

The ROC-curve

ROC-curves for comparison of logistic regression models

ROC-curves for comparison of tree-based models

Input selection based on the AUC

Another round of pruning based on AUC

Best of four

Further model reduction?

Course wrap-up

Pemodelan Risiko Kredit di R

Kursus
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