Oetama, Raymond Sunardi (2015) Enhancing Decision Tree Performance in Credit Risk Classification and Prediction. Ultimatics : Jurnal Teknik Informatika, 7 (1). ISSN 2085-4552
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Official URL: https://ejournals.umn.ac.id/index.php/TI/article/v...
Abstract
This study is focused on enhancing Decision Tree on its capabilities in classification as well as prediction. The capability of decision tree algorithm in classification outperforms its capability in prediction. The classification quality will be enhanced when it works with resampling techniques such as Adaboost.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming 600 Technology (Applied Sciences) > 600 Technology > 600 Technology |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 25 Nov 2021 08:40 |
| Last Modified: | 29 Jun 2022 02:51 |
| URI: | https://kc.umn.ac.id/id/eprint/19289 |
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