Performance Evaluation of Different Machine Learning Algorithms Using Credit Scoring Model

Abstract

The project focuses on the development of a credit scoring model. Concerns with credit scoring are being raised when developing an empirical model to support the financial decision-making process for financial institutions. This chapter focuses on the development of a credit scoring model using a combination of feature selection and ensemble classifiers. The most relevant features are identified, and an ensemble classifier is used to reduce the risk of overfitting with the aim of improving the classification performance of credit scoring models in the proposed method. Several metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, are used to evaluate the performance of the model. The accuracy and robustness of credit scoring models can potentially be improved by the proposed method, and the evaluation metrics can be used to further enhance it.