Under-sampled dataset.

<div><p>Credit scoring models play a crucial role for financial institutions in evaluating borrower risk and sustaining profitability. Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency; however, its predictive power...

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Bibliographic Details
Main Author:	Seongil Han (20490232) (author)
Other Authors:	Haemin Jung (20490235) (author)
Published:	2024
Subjects:	Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified understanding individual predictions providing detailed insights model &# 8217 managing class imbalance majority class tends imbalanced data distributions imbalanced class distributions explainability aspects within experimental results show evaluating borrower risk achieving optimal metrics credit risk classification predictive power decreases enhance model performance combining oversampling techniques integrates interpretability features enhancing model interpretability explainable credit scoring credit scoring due improving predictive performance parametric oversampling approach nate enhances interpretability credit scoring predictive performance parametric approach oversampling approaches transparent tool sustaining profitability reduced accuracy paper introduces nate ), making process logistic regression linear datasets gradient boosting framework designed financial institutions feature contributions f1 score crucial role computational efficiency comprehensively addresses based classifiers
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Under-sampled dataset.

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