| Yazarlar |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
| Özet |
| Superconducting materials conduct current with zero resistance at the superconducting critical temperature or temperatures lower than this temperature. Therefore, it is important to know the critical temperature of the superconductors. The aim of this study is to estimate the critical temperature of the superconducting materials with the gradient tree booster algorithm. For this purpose, the system named XGBoost (eXtreme Gradient Boosting) was used. The data set used in the study was created by Hamidieh (2018). This data set consists of 21.263 superconductor data with 81 attributes. 2/3 of the data set was used for training and the rest was used to test the XGBoost model. Four of the best models of the trained models were stored. Model performances were measured using RMSE and R2 metrics. The RMSE value for the test data of the best performing XGBoost model was calculated as 9.091 and the R2 was calculated as 0.928. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Uluslararası alan indekslerindeki dergilerde yayımlanan tam makale |
| Dergi Adı | UMYMK ICAIAME |
| Dergi Tarandığı Indeksler | |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2019 |
| Cilt No | 2019 |
| Sayfalar | 72 / 76 |
