Weight of evidence transformation in credit scoring models: How does it affect the discriminatory power?
(2021) STAN40 20201Department of Statistics
- Abstract
- Weight of evidence (WOE) transformation has been used for several decades in the credit industry. However, despite its widespread use, it has, surprisingly, been an overlooked approach in published literature. In this paper, we, therefore, investigate what effect WOE transformation has on the discriminatory power of a credit-scoring model. Our results suggest that using WOE transformation with logistic regression decreased the discriminatory power across a majority of the evaluation metrics compared to the models that did not use WOE transformed variables. Moreover, using an information value for variable selection did not provide any benefits over using the backward selection technique. However, applying support vector machine, we found... (More)
- Weight of evidence (WOE) transformation has been used for several decades in the credit industry. However, despite its widespread use, it has, surprisingly, been an overlooked approach in published literature. In this paper, we, therefore, investigate what effect WOE transformation has on the discriminatory power of a credit-scoring model. Our results suggest that using WOE transformation with logistic regression decreased the discriminatory power across a majority of the evaluation metrics compared to the models that did not use WOE transformed variables. Moreover, using an information value for variable selection did not provide any benefits over using the backward selection technique. However, applying support vector machine, we found mixed results depending on the preferred evaluation metric. Using an information value seems to provide some benefits regarding variable selection compared to the recursive feature elimination technique. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9066332
- author
- Persson, Rickard LU
- supervisor
- organization
- course
- STAN40 20201
- year
- 2021
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Credit Credit-scoring WOE weight of evidence Credit-scoring models
- language
- English
- id
- 9066332
- date added to LUP
- 2021-10-20 08:54:00
- date last changed
- 2021-10-20 08:54:00
@misc{9066332, abstract = {{Weight of evidence (WOE) transformation has been used for several decades in the credit industry. However, despite its widespread use, it has, surprisingly, been an overlooked approach in published literature. In this paper, we, therefore, investigate what effect WOE transformation has on the discriminatory power of a credit-scoring model. Our results suggest that using WOE transformation with logistic regression decreased the discriminatory power across a majority of the evaluation metrics compared to the models that did not use WOE transformed variables. Moreover, using an information value for variable selection did not provide any benefits over using the backward selection technique. However, applying support vector machine, we found mixed results depending on the preferred evaluation metric. Using an information value seems to provide some benefits regarding variable selection compared to the recursive feature elimination technique.}}, author = {{Persson, Rickard}}, language = {{eng}}, note = {{Student Paper}}, title = {{Weight of evidence transformation in credit scoring models: How does it affect the discriminatory power?}}, year = {{2021}}, }