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Comparative Analysis of ML in Credit Scoring by ngocvb234111e is a document available to read on EtoBox.

The final project report presents a comparative analysis of machine learning models in credit scoring, focusing on performance, explainability, and fairness. The study utilizes the Lending Club dataset from 2017-2018 and evaluates models such as Logistic Regression, Decision Trees, and Neural Networks using various metrics. Findings indicate that while ensemble models demonstrate higher predictive performance, they lack interpretability, highlighting the need for a balance between accuracy, explainability,

Author

ngocvb234111e

Language

EN