About this document
Machine Learning in Credit Risk Assessment by sivamathus24 is a document available to read on EtoBox.
This study compares four machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Machine (GBM)—for consumer credit risk assessment using a dataset of 10,000 credit accounts. The results indicate that GBM outperforms the others with an AUC of 0.87 and high classification accuracy, while Random Forest follows closely with an AUC of 0.85. The findings emphasize the advantages of machine learning over traditional methods in enhancing predictive accuracy and managing
- Author
- sivamathus24
- Language
- EN