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A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Taylor & Francis
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041569/ https://www.ncbi.nlm.nih.gov/pubmed/35706966 http://dx.doi.org/10.1080/02664763.2019.1643829 |