Hyper-parameter tuning of classification and regression trees for software effort estimation
محفوظ في:
| المؤلفون: | , , , |
|---|---|
| التنسيق: | comunicación de congreso |
| تاريخ النشر: | 2021 |
| الوصف: | Classification and regression trees (CART) have been reported to be competitive machine learning algorithms for software effort estimation. In this work, we analyze the impact of hyper-parameter tuning on the accuracy and stability of CART using the grid search, random search, and DODGE approaches. We compared the results of CART with support vector regression (SVR) and ridge regression (RR) models. Results show that tuning improves the performance of CART models up to a maximum of 0.153 standardized accuracy and reduce its stability radio to a minimum of 0.819. Also, CART proved to be as competitive as SVR and outperformed RR. |
| البلد: | Kérwá |
| المؤسسة: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| اللغة: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/102209 |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10669/102209 https://doi.org/10.1007/978-3-030-72660-7_56 |
| كلمة مفتاحية: | software effort estimation hyper-parameter tuning |