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Performance Evaluation of Different Machine Learning Algorithms Using Credit Scoring Model
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Author(s): Amrit Singh (NIST Institute of Science and Technology (Autonomous), India), Harisankar Mahapatra (NIST Institute of Science and Technology (Autonomous), India), Anil Kumar Biswal (Udayanath College of Science and Technology (Autonomous), India), Milan Samantaray (Trident Academy of Technology, India)and Debabrata Singh (Institute of Technical Education and Research, Siksha ‘O' Anusandhan (Deemed), India)
Copyright: 2023
Pages: 13
Source title:
The Software Principles of Design for Data Modeling
Source Author(s)/Editor(s): Debabrata Samanta (Rochester Institute of Technology, Kosovo)
DOI: 10.4018/978-1-6684-9809-5.ch018
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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.
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