Feature Selection in a Credit Scoring Model

Feature Selection in a Credit Scoring Model

This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate...

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Bibliographic Details
Main Authors: Juan Laborda, Seyong Ryoo
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Mathematics
Subjects:
operational research in banking
machine learning
credit scoring
classification algorithms
feature selection methods
Online Access:https://www.mdpi.com/2227-7390/9/7/746

Internet

https://www.mdpi.com/2227-7390/9/7/746

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