- Title
- Statistical classification, an application to credit default
- Creator
- Sikhakhane, Anele Gcina
- ThesisAdvisor
- Baxter, J.
- Subject
- Uncatalogued
- Date
- 2024-10-11
- Type
- Academic theses
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10962/465069
- Identifier
- vital:76570
- Description
- Statistical learning has been used in both industry and academia to create credit scoring models. These models are used to predict who might default on their loan repayments, thus minimizing the risk financial institutions face. In this study six traditional and one more recent classifier, namely kNN, LDA, CART, RF, AdaBoost, XGBoost and SynBoost were used to predict who might default on their loans. The data set used in this study was imbalanced thus sampling and performance evaluation techniques were investigated and used to balance the class distribution and assess the classifiers performance. In addition to the standard variables and data set, new variables called synthetic variables and synthetic data sets were produced, investigated and used to predict who might default on their loans. This study found that the synthetic data set had strong predictive power and sampling methods negatively affected the classifiers performance. The best-performing classifier was XGBoost, with an AUC score of 0.7732.
- Description
- Thesis (MSc) -- Faculty of Science, Statistics, 2024
- Format
- computer, online resource, application/pdf, 1 online resource (160 pages), pdf
- Publisher
- Rhodes University, Faculty of Science, Statistics
- Language
- English
- Rights
- Sikhakhane, Anele Gcina
- Rights
- Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-ShareAlike" License (http://creativecommons.org/licenses/by-nc-sa/2.0/)
- Hits: 119
- Visitors: 125
- Downloads: 6
Collections
RU Department of Statistics
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | SIKHAKHANE-MSC-TR24-206.pdf | 1 MB | Adobe Acrobat PDF | View Details |